Computational Model Research Articles

A GFDM based computational model for the analysis of tunnels under gravitational loadings

Based on an impedance relation, a new computational model is developed for the analysis of tunnels under gravitational loading. The shape of tunnel is arbitrary. The impedance matrix, representing the soil resistance, is evaluated through integration of the equations of elasticity by generalized finite difference method (GFDM). The rigidity matrix of tunnel ring is constructed by using two types of elements: plate and shell elements. The equations of these elements are evaluated through integration of the equations of higher order plate and shell theories again by GFDM. These element equations accommodate not only axial and bending deformations, but, also shear deformations in the ring. In the study, the rotational joint deformation is simulated through the use of rotational spring model and the slippage is considered by modifying the impedance matrix so that the tangential soil resistance force between the ring and soil medium vanishes. Some numerical results are presented, in nondimensional forms, for circular and square tunnels where the following three cases are considered for circular tunnels: a) the soil medium is infinite, the change in σv (vertical (in situ) compressional stress) in the vicinity of tunnel is disregarded b) the soil medium is infinite, the change in σv is taken into account c) the soil medium is halfspace (HS). In connection with the case of (a), a comparison is presented for the ring flexibility curves of radial displacement and section forces of circular ring with those reported in literature. In view of the interpretations of the results and comparisons, we think, the proposed model may be used reliably in the analysis of tunnels.

Delayed non‐fragile robust control of civil structure considering structural uncertainties, actuator faults, and control force time delays

Active control is an effective strategy for suppressing unwanted structural vibrations. The uncertainties in computational models, actuator faults, and time delays in control forces are the potential threats for control performance improvements. These factors are interconnected and may lead to control system instability and poor control effectiveness. The paper presents a delayed non-fragile robust control (DNR) algorithm for uncertain systems with consideration of actuator faults and time delays. To this end, the existence conditions for the DNR multi-objective controller are derived by utilizing the Lyapunov stability proof and introducing a pole constraint inequality. Then, a dynamic output feedback DNR algorithm is further developed. Finally, two numerical simulation examples are discussed for several ground motions. The results show that the control system based on the proposed algorithm exhibits better stability, robustness, and fault tolerance than the linear quadratic Gaussian (LQG) algorithm. Furthermore, there is no need for additional control energy. Significantly, the control performance of the proposed algorithm can be further enhanced with the decentralized control strategy.

A new experimental and numerical simulation method for measuring ash slag fluidity using VOF based on OpenFOAM

A numerical computational model of heated ash flow is proposed using the Volume of Fluid (VOF) method based on OpenFOAM. Established numerical model can accurately calculate the critical flow temperature (Tc) and critical flow velocity (Vc) of ash slag with the error of 1.5 % and 19 % from the experiment, respectively. Forty-five types of ash slag with widely varied properties in China were calculated based on the established model. Results show that high levels (>30 wt%) of Fe2O3 and CaO improve the flowability of the ash slag, and low levels of Al2O3 (10 wt%) also contribute to the flowability of the ash slag. Tc and Vc of the ash were distributed in the range of 1150 ∼ 1400 °C and 0.1 ∼ 1.0 mm/min, respectively. Tc and Vc change in opposite trends with the component content increases, indicating that a higher Tc corresponds to a lower Vc and fluidity reduction. Mathematical models for calculating the Tc and Vc of ash were obtained based on non-linear multiple regression and correlation coefficient, and prediction accuracy was within 10 % and 30 % for Tc and Vc, respectively. The proposed numerical simulation method and prediction model can provide an accurate reference for a wide range of ash fluidity. Also, it was demonstrated that elemental Na has a positive effect on ash flowability based on the model, which is important for measuring slag flowability in slag-tap furnaces.

Bio-inspired algorithms for feature engineering: analysis, applications and future research directions

Purpose Nature’s evolution has shaped intelligent behaviors in creatures like insects and birds, inspiring the field of Swarm Intelligence. Researchers have developed bio-inspired algorithms to address complex optimization problems efficiently. These algorithms strike a balance between computational efficiency and solution optimality, attracting significant attention across domains. Design/methodology/approach Bio-inspired optimization techniques for feature engineering and its applications are systematically reviewed with chief objective of assessing statistical influence and significance of “Bio-inspired optimization”-based computational models by referring to vast research literature published between year 2015 and 2022. Findings The Scopus and Web of Science databases were explored for review with focus on parameters such as country-wise publications, keyword occurrences and citations per year. Springer and IEEE emerge as the most creative publishers, with indicative prominent and superior journals, namely, PLoS ONE, Neural Computing and Applications, Lecture Notes in Computer Science and IEEE Transactions. The “National Natural Science Foundation” of China and the “Ministry of Electronics and Information Technology” of India lead in funding projects in this area. China, India and Germany stand out as leaders in publications related to bio-inspired algorithms for feature engineering research. Originality/value The review findings integrate various bio-inspired algorithm selection techniques over a diverse spectrum of optimization techniques. Anti colony optimization contributes to decentralized and cooperative search strategies, bee colony optimization (BCO) improves collaborative decision-making, particle swarm optimization leads to exploration-exploitation balance and bio-inspired algorithms offer a range of nature-inspired heuristics.

An experimentally informed computational model of neurovestibular adaptation to altered gravity.

Transitions to altered gravity environments result in acute sensorimotor impairment for astronauts, leading to serious mission and safety risks in the crucial first moments in a new setting. Our understanding of the time course and severity of impairment in the early stages of adaptation remains limited and confounded by unmonitored head movements, which are likely to impact the rate of adaptation. Here, we aimed to address this gap by using a human centrifuge to simulate the first hour of hypergravity (1.5g) exposure and the subsequent 1g readaptation period, with precisely controlled head tilt activity. We quantified head tilt overestimation via subjective visual vertical and found ∼30% tilt overestimation that did not decrease over the course of 1h of exposure to the simulated gravity environment. These findings extended the floor of the vestibular adaptation window (with controlled vestibular cueing) to 1h of exposure to altered gravity. We then used the empirical data to inform a computational model of neurovestibular adaptation to changes in the magnitude of gravity, which can offer insight into the adaptation process and, with further tuning, can be used to predict the temporal dynamics of vestibular-mediated misperceptions in altered gravity.

Multi-scale modelling of the epileptic brain: advantages of computational therapy exploration.

Epilepsy is a complex disease spanning across multiple scales, from ion channels in neurons to neuronal circuits across the entire brain. Over the past decades, computational models have been used to describe the pathophysiological activity of the epileptic brain from different aspects. Traditionally, each computational model can aid in optimizing therapeutic interventions, therefore, providing a particular view to design strategies for treating epilepsy. As a result, most studies are concerned with generating specific models of the epileptic brain that can help us understand the certain machinery of the pathological state. Those specific models vary in complexity and biological accuracy, with system-level models often lacking biological details. Here, we review various types of computational models of epilepsy and discuss their potential for different therapeutic approaches and scenarios, including drug discovery, surgical strategies, brain stimulation, and seizure prediction. We propose that we need to consider an integrated approach with a unified modelling framework across multiple scales to understand the epileptic brain. Our proposal is based on the recent increase in computational power, which has opened up the possibility of unifying those specific epileptic models into simulations with an unprecedented level of detail. A multi-scale epilepsy model can bridge the gap between biologically detailed models, used to address molecular and cellular questions, and brain-wide models based on abstract models which can account for complex neurological and behavioral observations. With these efforts, we move toward the next generation of epileptic brain models capable of connecting cellular features, such as ion channel properties, with standard clinical measures such as seizure severity.

Effect of schooling on flow generated sounds from carangiform swimmers

Computational models are used to examine the effect of schooling on flow generated noise from fish swimming using their caudal fins. We simulate the flow as well as the far-field hydrodynamic sound generated by the time-varying pressure loading on these carangiform swimmers. The effect of the number of swimmers in the school, the relative phase of fin flapping of the swimmers, and their spatial arrangement is examined. The simulations indicate that the phase of the fin flapping is a dominant factor in the total sound radiated into the far-field by a group of swimmers. For small schools, a suitable choice of relative phase between the swimmers can significantly reduce the overall intensity of the sound radiated to the far-field. The relative positioning of the swimmers is also shown to have an impact on the total radiated noise. For a larger school, even highly uncorrelated phases of fin movement between the swimmers in the school are very effective in significantly reducing the overall intensity of sound radiated into the far-field. The implications of these findings for fish ethology as well as the design and operation of bioinspired vehicles are discussed.

Updating Nonlinear Stochastic Dynamics of an Uncertain Nozzle Model using Probabilistic Learning with Partial Observability and Incomplete dataset

Abstract This paper introduces a methodology for updating the nonlinear stochastic dynamics of a nozzle with uncertain computational model. The approach focuses on a high-dimensional nonlinear computational model constrained by a small target dataset. Challenges include the large number of degrees-of-freedom, geometric nonlinearities, material uncertainties, stochastic external loads, under-observability, and high computational costs. A detailed dynamic analysis of the nozzle is presented. An updated statistical surrogate model relating the observations of interest to the control parameters is constructed. Despite small training and target datasets, and partial observability, the study successfully applies Probabilistic Learning on Manifolds (PLoM) to address these challenges. PLoM captures geometric nonlinear effects and uncertainty propagation, improving conditional mean statistics compared to training data. The conditional confidence region demonstrates the ability of the methodology to accurately represent both observed and unobserved output variables, contributing to advancements in modeling complex systems.

Mathematical Modeling and Econometric Analysis of Composite Fuel Combustion Process

In this article, the process of combustion of composite fuel is studied. Particles of hydrocarbon fuel were taken as the object of research. The analysis of the combustion process is carried out on the basis of mathematical modeling. The created mathematical model was solved by the numerical method of finite elements using the Elcut program, and a computer model was developed. The results obtained on the basis of the numerical method were compared with the experimental results, as well as an econometric analysis and forecast based on the regression model. A comparison of the results obtained by the finite element method using the Elcut program with experimental data shows that the error is on average 5% and indicates the physical validity of the equations and boundary conditions (1)-(7) for the mathematical modeling of the considered tasks and use of the Elcut program for numerical calculations.

Local and global changes in cell density induce reorganisation of 3D packing in a proliferating epithelium.

Tissue morphogenesis is intimately linked to the changes in shape and organisation of individual cells. In curved epithelia, cells can intercalate along their own apicobasal axes adopting a shape named "scutoid" that allows energy minimization in the tissue. Although several geometric and biophysical factors have been associated with this 3D reorganisation, the dynamic changes underlying scutoid formation in 3D epithelial packing remain poorly understood. Here we use live-imaging of the sea star embryo coupled with deep learning-based segmentation, to dissect the relative contributions of cell density, tissue compaction, and cell proliferation on epithelial architecture. We find that tissue compaction, which naturally occurs in the embryo, is necessary for the appearance of scutoids. Physical compression experiments identify cell density as the factor promoting scutoid formation at a global level. Finally, the comparison of the developing embryo with computational models indicates that the increase in the proportion of scutoids is directly associated with cell divisions. Our results suggest that apico-basal intercalations appearing just after mitosis may help accommodate the new cells within the tissue. We propose that proliferation in a compact epithelium induces 3D cell rearrangements during development.

An enhanced deep learning approach for vascular wall fracture analysis

AbstractThis work outlines an efficient deep learning approach for analyzing vascular wall fractures using experimental data with openly accessible source codes (https://doi.org/10.25835/weuhha72) for reproduction. Vascular disease remains the primary cause of death globally to this day. Tissue damage in these vascular disorders is closely tied to how the diseases develop, which requires careful study. Therefore, the scientific community has dedicated significant efforts to capture the properties of vessel wall fractures. The symmetry-constrained compact tension (symconCT) test combined with digital image correlation (DIC) enabled the study of tissue fracture in various aorta specimens under different conditions. Main purpose of the experiments was to investigate the displacement and strain field ahead of the crack tip. These experimental data were to support the development and verification of computational models. The FEM model used the DIC information for the material parameters identification. Traditionally, the analysis of fracture processes in biological tissues involves extensive computational and experimental efforts due to the complex nature of tissue behavior under stress. These high costs have posed significant challenges, demanding efficient solutions to accelerate research progress and reduce embedded costs. Deep learning techniques have shown promise in overcoming these challenges by learning to indicate patterns and relationships between the input and label data. In this study, we integrate deep learning methodologies with the attention residual U-Net architecture to predict fracture responses in porcine aorta specimens, enhanced with a Monte Carlo dropout technique. By training the network on a sufficient amount of data, the model learns to capture the features influencing fracture progression. These parameterized datasets consist of pictures describing the evolution of tissue fracture path along with the DIC measurements. The integration of deep learning should not only enhance the predictive accuracy, but also significantly reduce the computational and experimental burden, thereby enabling a more efficient analysis of fracture response.

Design and Research of Computer Model of Wind Turbine Using LabVIEW

Design and Research of Computer Model of Wind Turbine Using LabVIEW

Monocular reconstruction of shapes of natural objects from orthographic and perspective images

Human subjects were tested in perception of shapes of 3D objects. The subjects reconstructed 3D shapes by viewing orthographic and perspective images. Perception of natural shapes was very close to veridical and was clearly better than perception of random symmetrical polyhedra. Viewing perspective images led to only slightly better performance than viewing orthographic images. In order to account for subjects’ performance, we elaborated the previous computational models of 3D shape reconstruction. The previous models used as constraints mirror-symmetry and 3D compactness. The critical additional constraint was the use of a secondary mirror-symmetry that exists in most natural shapes. It is known that two planes of mirror symmetry are sufficient for a unique and veridical shape reconstruction. We also generalized the model so that it applies to both orthographic and perspective images. The results of our experiment suggest that the human visual system uses two planes of symmetry in addition to two forms of 3D compactness. Performance of the new model was highly correlated with subjects’ performance with both orthographic and perspective images, which supports the claim that the most important 3D shape constraints that are used by the human visual system have been identified.

Model of energy consumption by brake discs of rail vehicles

The aim of the article is to present a computational model to determine the losses generated by the brake discs of rail vehicles. The energy consumption of the disc (energy losses) is divided into inertia losses and fan losses. The article is preceded by a review of the design solutions of brake discs of rail vehicles in terms of their ventilation and cooling after braking. Particular attention was paid to three types of brake disc designs with ventilation blades, bars and other shapes of ventilation bridges, and how the ventilation of these discs affects energy losses. On the one hand, forced ventilation of brake discs shortens the cooling time after intensive braking, on the other hand, it causes energy losses due to forced ventilation when the brakes are not used. The article presents the state of knowledge regarding the cooling efficiency of brake discs as a result of forced ventilation and presents a method for calculating the energy consumption generated by the brake disc in relation to the rotational speed. The energy losses were divided in the article into inertia losses due to the rotating mass and losses on the fan, in which the energy necessary for the rotation of the brake disc was determined from the method of calculating centrifugal fans with known geometric dimensions.

Post-Translational Modifications in Tau and Their Roles in Alzheimer's Pathology.

Microtubule-Associated Protein Tau (also known as tau) has been shown to accumulate into paired helical filaments and neurofibrillary tangles, which are known hallmarks of Alzheimer's disease (AD) pathology. Decades of research have shown that tau protein undergoes extensive post-translational modifications (PTMs), which can alter the protein's structure, function, and dynamics and impact the various properties such as solubility, aggregation, localization, and homeostasis. There is a vast amount of information describing the impact and role of different PTMs in AD pathology and neuroprotection. However, the complex interplay between these PTMs remains elusive. Therefore, in this review, we aim to comprehend the key post-translational modifications occurring in tau and summarize potential connections to clarify their impact on the physiology and pathophysiology of tau. Further, we describe how different computational modeling methods have helped in understanding the impact of PTMs on the structure and functions of the tau protein. Finally, we highlight the tau PTM-related therapeutics strategies that are explored for the development of AD therapy.

Substrate geometry affects population dynamics in a bacterial biofilm

Biofilms inhabit a range of environments, such as dental plaques or soil micropores, often characterized by noneven surfaces. However, the impact of surface irregularities on the population dynamics of biofilms remains elusive, as most experiments are conducted on flat surfaces. Here, we show that the shape of the surface on which a biofilm grows influences genetic drift and selection within the biofilm. We culture Escherichia coli biofilms in microwells with a corrugated bottom surface and observe the emergence of clonal sectors whose size corresponds to that of the corrugations, despite no physical barrier separating different areas of the biofilm. The sectors are remarkably stable and do not invade each other; we attribute this stability to the characteristics of the velocity field within the biofilm, which hinders mixing and clonal expansion. A microscopically detailed computer model fully reproduces these findings and highlights the role of mechanical interactions such as adhesion and friction in microbial evolution. The model also predicts clonal expansion to be limited even for clones with a significant growth advantage-a finding which we confirm experimentally using a mixture of antibiotic-sensitive and antibiotic-resistant mutants in the presence of sublethal concentrations of the antibiotic rifampicin. The strong suppression of selection contrasts sharply with the behavior seen in range expansion experiments in bacterial colonies grown on agar. Our results show that biofilm population dynamics can be affected by patterning the surface and demonstrate how a better understanding of the physics of bacterial growth can be used to control microbial evolution.

High Hydrogen Storage in Trigonal Prismatic Monomer-based Highly Porous Aromatic Frameworks.

Hydrogen storage is crucial in the shift toward a carbon-neutral society, where hydrogen serves as a pivotal renewable energy source. Utilizing porous materials can provide an efficient hydrogen storage solution, reducing tank pressures to manageable levels and circumventing the energy-intensive and costly current technological infrastructure. Herein, we report two highly porous aromatic frameworks (PAFs), C-PAF and Si-PAF, prepared through a Yamamoto C-C coupling reaction between trigonal prismatic monomers. These PAFs exhibited large pore volumes and BET areas, 3.93 cm3 g-1 and 4857 m2 g-1 for C-PAF, and 3.80 cm3 g-1 and 6099 m2 g-1 for Si-PAF, respectively. Si-PAF exhibited a record-high gravimetric hydrogen delivery capacity of 17.01 wt% and a superior volumetric capacity of 46.5g L-1 under pressure-temperature swing adsorption conditions (77 K, 100bar → 160 K, 5bar), outperforming benchmark hydrogens storage materials. By virtue of the robust C-C covalent bond, both PAFs showed impressive structural stabilities in harsh environments and unprecedented long-term durability. Computational modeling methods were employed to simulate and investigate the structural and adsorption properties of the PAFs. These results demonstrate that C-PAF and Si-PAF are promising materials for efficient hydrogen storage. This article is protected by copyright. All rights reserved.

ОСОБЛИВОСТІ ПРОЄКТУВАННЯ МЕХАНІЗАЦІЇ КРИЛА ЛІТАКІВ ТРАНСПОРТНОЇ КАТЕГОРІЇ

The paper analyses the methods of designing wing mechanisation for transport category aircraft. The wing layout with the placement of wing mechanisation means on the leading and trailing edges of the wing is analysed. Specific requirements for wing mechanisation units are considered. The analysis takes into account the requirements of aircraft operation, which provide easy access to inspect the mechanisation units and their attachment mechanisms. The need for constant monitoring of the synchronous operation of all mechanisation sections and ensuring their smooth movement is emphasised. The design of any type of mechanisation means involves the simultaneous solution of a number of development tasks: kinematic scheme, installation and control system, support structure and the device itself. In most cases, the drive mechanisms are also developed. The design of a new aircraft is a multi-stage iterative process that includes external design, development of a technical proposal, preliminary design, detailed design, production of prototypes, ground and flight tests, which determine the actual characteristics of the aircraft and the degree of its compliance with the technical specifications. Consider the advantages created by wing mechanisation equipment. The mechanisation of the AN-140 is considered as a representative of aircraft whose wings have trailing edge mechanisation but no leading edge mechanisation. The mechanisation of the AN-74 is also considered as a representative of aircraft that use two- or three-slotted retractable flaps, winglets or nose shields of large curvature over the entire wing span. The design technologies in integrated computer information systems are characterised, which allow to build a single process of computer modelling and engineering analysis and to conduct multivariate studies to select optimal design solutions in a sufficiently short time.  A block diagram of the process of detailed design of flaps is proposed.

Mechanistic regulation of planarian shape during growth and degrowth.

Adult planarians can grow when fed and degrow (shrink) when starved while maintaining their whole-body shape. It is unknown how the morphogens patterning the planarian axes are coordinated during feeding and starvation or how they modulate the necessary differential tissue growth or degrowth. Here we investigate the dynamics of planarian shape together with a theoretical study of the mechanisms regulating whole-body proportions and shape. We found that the planarian body proportions scale isometrically following similar linear rates during growth and degrowth, but that fed worms are significantly wider than starved worms. By combining a descriptive model of planarian shape and size with a mechanistic model of anterior-posterior and medio-lateral signaling calibrated with a novel parameter optimization methodology, we theoretically demonstrate that the feedback loop between these positional information signals and the shape they control can regulate the planarian whole-body shape during growth. Furthermore, the computational model produced the correct shape and size dynamics during degrowth due to a predicted increase in apoptosis rate and pole signal during starvation. These results offer mechanistic insights into the dynamic regulation of whole-body morphologies.

A scalable spiking amygdala model that explains fear conditioning, extinction, renewal and generalization.

The amygdala (AMY) is widely implicated in fear learning and fear behaviour, but it remains unclear how the many biological components present within AMY interact to achieve these abilities. Building on previous work, we hypothesize that individual AMY nuclei represent different quantities and that fear conditioning arises from error-driven learning on the synapses between AMY nuclei. We present a computational model of AMY that (a) recreates the divisions and connections between AMY nuclei and their constituent pyramidal and inhibitory neurons; (b) accommodates scalable high-dimensional representations of external stimuli; (c) learns to associate complex stimuli with the presence (or absence) of an aversive stimulus; (d) preserves feature information when mapping inputs to salience estimates, such that these estimates generalize to similar stimuli; and (e) induces a diverse profile of neural responses within each nucleus. Our model predicts (1) defensive responses and neural activities in several experimental conditions, (2) the consequence of artificially ablating particular nuclei and (3) the tendency to generalize defensive responses to novel stimuli. We test these predictions by comparing model outputs to neural and behavioural data from animals and humans. Despite the relative simplicity of our model, we find significant overlap between simulated and empirical data, which supports our claim that the model captures many of the neural mechanisms that support fear conditioning. We conclude by comparing our model to other computational models and by characterizing the theoretical relationship between pattern separation and fear generalization in healthy versus anxious individuals.