Academic literature on the topic '3D dynamical fibre networks'

Nowadays, in vitro three-dimensional (3D) neuronal networks are becoming a consolidated experimental model to overcome most of the intrinsic limitations of bi-dimensional (2D) assemblies. In the 3D environment, experimental evidence revealed a wider repertoire of activity patterns, characterized by a modulation of the bursting features, than the one observed in 2D cultures. However, it is not totally clear and understood what pushes the neuronal networks towards different dynamical regimes. One possible explanation could be the underlying connectivity, which could involve a larger number of neurons in a 3D rather than a 2D space and could organize following well-defined topological schemes. Driven by experimental findings, achieved by recording 3D cortical networks organized in multi-layered structures coupled to Micro-Electrode Arrays (MEAs), in the present work we developed a large-scale computational network model made up of leaky integrate-and-fire (LIF) neurons to investigate possible structural configurations able to sustain the emerging patterns of electrophysiological activity. In particular, we investigated the role of the number of layers defining a 3D assembly and the spatial distribution of the connections within and among the layers. These configurations give rise to different patterns of activity that could be compared to the ones emerging from real in vitro 3D neuronal populations. Our results suggest that the introduction of three-dimensionality induced a global reduction in both firing and bursting rates with respect to 2D models. In addition, we found that there is a minimum number of layers necessary to obtain a change in the dynamics of the network. However, the effects produced by a 3D organization of the cells is somewhat mitigated if a scale-free connectivity is implemented in either one or all the layers of the network. Finally, the best matching of the experimental data is achieved supposing a 3D connectivity organized in structured bundles of links located in different areas of the 2D network.

The analysis of fibrous structures using micro-computer tomography (µCT) is becoming more important as it provides an opportunity to characterise the mechanical properties and performance of materials. This study is the first attempt to provide computations of fibre crimp for various random fibrous networks (RFNs) based on µCT data. A parametric algorithm was developed to compute fibre crimp in fibres in a virtual domain. It was successfully tested for six different X-ray µCT models of nonwoven fabrics. Computations showed that nonwoven fabrics with crimped fibres exhibited higher crimp levels than those with non-crimped fibres, as expected. However, with the increased fabric density of the non-crimped nonwovens, fibres tended to be more crimped. Additionally, the projected fibre crimp was computed for all three major 2D planes, and the obtained results were statistically analysed. Initially, the algorithm was tested for a small-size, nonwoven model containing only four fibres. The fraction of nearly straight fibres was computed for both crimped and non-crimped fabrics. The mean value of the fibre crimp demonstrated that fibre segments between intersections were almost straight. However, it was observed that there were no perfectly straight fibres in the analysed RFNs. This study is applicable to approach employing a finite-element analysis (FEA) and computational fluid dynamics (CFD) to model/analyse RFNs.

Abstract By using X-ray synchrotron microtomography imaging, this work aims at proposing a method to get 3D information on paper fibrous microstructures. Such technique is useful to better understand the links between the manufacturing conditions, the resulting microstructural and mechanical properties of the paper fibrous networks, together with the morphology of fibres and fibre-fibre bonds. Its usefulness is illustrated for the 3D analysis of model papers being produced by changing the wet pressing conditions. It is demonstrated that the image analysis allows the changes of parameters describing, for example, the fibre cross section shape and inclination, the bond area surfaces, the distance between bonds to be followed with respect to the processing conditions for a large set of fibres and bonds. The distributions of properties that can be drawn from this experimental analysis will allow mechanical or physical discrete modelling approaches for papers to be enriched.

Fibre topography of the extracellular matrix governs local mechanical properties and cellular behaviour including migration and gene expression. While quantifying properties of the fibrous network provides valuable data that could be used across a breadth of biomedical disciplines, most available techniques are limited to two dimensions and, therefore, do not fully capture the architecture of three-dimensional (3D) tissue. The currently available 3D techniques have limited accuracy and applicability and many are restricted to a specific imaging modality. To address this need, we developed a novel fibre analysis algorithm capable of determining fibre orientation, fibre diameter and fibre branching on a voxel-wise basis in image stacks with distinct fibre populations. The accuracy of the technique is demonstrated on computer-generated phantom image stacks spanning a range of features and complexities, as well as on two-photon microscopy image stacks of elastic fibres in bovine tendon and dermis. Additionally, we propose a measure of axial spherical variance which can be used to define the degree of fibre alignment in a distribution of 3D orientations. This method provides a useful tool to quantify orientation distributions and variance on image stacks with distinguishable fibres or fibre-like structures.

We describe 3-dimensional dynamical systems with piecewise linear right hand sides which simulate functioning of simplest molecualr repressilators and contain infinite oneparametric families of cycles in their phase portraits. An analogous dynamical system with step functions in its right hand sides is constructed; its phase portrait contains two piecewise linear cycles. A surface separating these two cycles is described.

A three-dimensional (3D) ZnO-supported carbon fiber aerogel (ZnO/CFA) was successfully prepared by using natural cotton with hydrophilicity as the precursor. The facile synthetic strategy includes two steps: Zn2+ exchange on the surface of cotton and thermal treatment at high temperatures. Particularly, the calcination temperature was found to greatly affect the content, dispersity, and size of supported ZnO nanoparticles, and the product obtained at 600 °C (ZnO/CFA-600) exhibited both high ZnO loading and well-dispersed ZnO nanoparticles. Therefore, ZnO/CFA-600 has superior photocatalytic activity for tetracycline (TC) degradation under UV light irradiation compared with others. Additionally, the unique 3D crosslinking network inside the ZnO/CFA generates an open channel for the rapid migration and diffusion of reactants and products. In a dynamical water-treated system, the 3D porous ZnO/CFA-600 continuously works for TC removal without any separation operation and maintains high synergistic performance of adsorption and photocatalysis for at least 8 h. Consequently, the 3D porous ZnO/CFA product, with its large adsorbability and high photoactivity, shows a lot of industrial potential in wastewater treatments.

The paper stresses the universal role that Cellular Nonlinear Networks (CNNs) are assuming today. It is shown that the dynamical behavior of 3D CNN-based models allows us to approach new emerging problems, to open new research frontiers as the generation of new geometrical forms and to establish some links between art, neuroscience and dynamical systems.

The vasculature plays a central role as the highway of the body, through which nutrients and oxygen as well as biochemical factors and signals are distributed by blood flow. Therefore, understanding the flow and distribution of particles inside the vasculature is valuable both in healthy and disease-associated networks. By creating models that mimic the microvasculature fundamental knowledge can be obtained about these parameters. However, microfabrication of such models remains a challenging goal. In this paper we demonstrate a promising 3D sugar printing method that is capable of recapitulating the vascular network geometry with a vessel diameter range of 1 mm down to 150 µm. For this work a dedicated 3D printing setup was built that is capable of accurately printing the sugar glass material with control over fibre diameter and shape. By casting of printed sugar glass networks in PDMS and dissolving the sugar glass, perfusable networks with circular cross-sectional channels are obtained. Using particle image velocimetry, analysis of the flow behaviour was conducted showing a Poisseuille flow profile inside the network and validating the quality of the printing process.

Lens-based endoscopes offer high lateral resolution, but suffer from rigid imaging properties, such as a fixed focal plane. We present a miniaturized 0.5 mm diameter endoscope in which the objective lens is replaced by an optical diffuser. The intensity information of the object space is scattered and passed to a camera via a coherent fibre bundle. The image is reconstructed by a neural network. The field of view and resolution depend on the object distance. 3D-single-shot imaging up to video rate can be enabled. The approach shows great potential for applications like robust 3D fluorescence imaging.

Cette thèse porte sur l'hypothèse que des interactions mécaniques locales simples entre un nombre limité de composants puissent régir l'émergence de l'architecture 3D des tissus biologiques. Pour explorer cette possibilité, nous développons deux modèles mathématiques. Le premier, ECMmorpho-3D, vise à reproduire un tissu conjonctif non-spécialisé réduit à la matrice extra-cellulaire, c'est-à-dire à un réseau 3D de fibres interconnectées dynamiquement. Le second, ATmorpho-3D, est obtenu par ajout de cellules sphériques apparaissant et croissant spontanément dans ce réseau de fibres afin de modéliser la morphogenèse du tissu adipeux, un tissu conjonctif spécialisé ayant une grande importance sur le plan biomédical. Pour analyser les données produites par ces deux modèles, nous construisons un outil générique permettant de visualiser en 3D des systèmes composés d'un mélange d'éléments sphériques (cellules) et de bâtonnets (fibres) et de détecter automatiquement dans de tels systèmes des amas d'objets sphériques séparés par des bâtonnets. Cet outil peut également être utilisé pour traiter des images biologiques issues de microscopie en 3D, permettant ainsi une comparaison directe entre les structures in vivo et in silico. L'étude des structures produites par le modèle ECMmorpho-3D via des simulations numériques montre que ce modèle peut générer spontanément différents types d'architectures, que nous identifions et caractérisons grâce à notre outil d'analyse. Une analyse paramétrique approfondie nous permet d'identifier une variable émergente, le nombre de liens par fibre, qui explique et, dans une certaine mesure, prédit le devenir du système modélisé. Une analyse temporelle révèle que l'échelle de temps caractéristique de ce processus d'auto-organisation est fonction de la vitesse de remodelage du réseau et que tous les systèmes suivent la même trajectoire évolutive. Enfin, nous utilisons le modèle ATmorpho-3D pour explorer l'influence de cellules sphériques sur l'organisation d'un réseau de fibres dynamique, en prenant comme référence le modèle ECMmorpho-3D. Nous montrons que le nombre de cellules influence l'alignement local des fibres mais pas l'organisation globale du réseau. Par ailleurs, les cellules s'organisent spontanément en amas entourés de feuillets de fibres, dont les caractéristiques morphologiques sont très proches de celles des structures cellulaires in vivo. De plus, la distribution des différentes morphologies d'amas cellulaires est similaire dans les systèmes in silico et in vivo. Ceci suggère que le modèle est capable de produire des morphologies réalistes non seulement à l'échelle d'un amas mais aussi à l'échelle du système entier, en reproduisant les variabilités structurelles observées dans les échantillons biologiques. Une analyse paramétrique révèle que la proportion de chaque morphologie dans un système in silico est gouvernée principalement par les capacités de remodelage du réseau de fibres, pointant le rôle essentiel des propriétés de la matrice extra-cellulaire dans l'architecture et le fonctionnement du tissu adipeux (ce qui concorde avec plusieurs constatations biologiques ainsi que des résultats antérieurs en 2D). Le fait que ces modèles mathématiques très simples puissent générer des structures réalistes corrobore notre hypothèse selon laquelle l'architecture des tissus biologiques pourrait émerger spontanément à partir d'interactions mécaniques locales entre les composants du tissu, indépendamment des phénomènes biologiques complexes se déroulant dans ce tissu. Ce travail ouvre de nombreuses perspectives quant à notre compréhension des principes fondamentaux gouvernant la manière dont l'architecture d'un tissu émerge durant l'organogenèse, est maintenue au cours de la vie et peut être affectée par diverses pathologies. Les applications potentielles vont de l'ingénierie tissulaire à la possibilité de promouvoir la régénération chez les mammifères adultes
In this thesis, we investigate whether simple local mechanical interactions between a reduced set of components could govern the emergence of the 3D architecture of biological tissues. To explore this hypothesis, we develop two mathematical models. The first one, ECMmorpho-3D, aims at reproducing a non-specialised connective tissue and is reduced to the Extra-Cellular Matrix (ECM) component, that is a 3D dynamically connected fibre network. The second, ATmorpho-3D, is built by adding to this network spherical cells which spontaneously appear and grow in order to mimic the morphogenesis of Adipose Tissue (AT), a specialised connective tissue with major biomedical importance. We then construct a unified analysis framework to visualise, segment and quantitatively characterise the fibrous and cellular structures produced by our two models. It constitutes a generic tool for the 3D visualisation of systems composed of a mixture of spherical (cells) and rod-like (fibres) elements and for the automatic detection of in such systems of clusters of spherical objects separated by rod-like elements. This tool is also applicable to biological 3D microscopy images, enabling a comparison between in vivo and in silico structures. We study the structures produced by the model ECMmorpho-3D by performing numerical simula- tions. We show that this model is able to spontaneously generate different types of architectures, which we identify and characterise using our analysis framework. An in-depth parametric analysis lead us to identify an intermediate emerging variable, the number of crosslinks per fibre, which explains and partly predicts the fate of the modelled system. A temporal analysis reveals that the characteristic time-scale of the organisation process is a function of the network remodelling speed, and that all systems follow the same, unique evolutionary pathway. Finally, we use the model ATmorpho-3D to explore the influence of round cells over the organisation of a fibre network, taking as reference the model ECMmorpho-3D. We show that the number of cells can influence the local alignment of the fibres but not the global organisation of the network. On the other hand, the cells inside the network spontaneously organise into clusters with realistic morphological features very close to those of in vivo structures, surrounded by sheet-like fibre bundles. Moreover, the distribution of the different morphological types of clusters is similar in in silico and in vivo systems, suggesting that the model is able to produce realistic morphologies not only on the scale of one cluster but also on the scale of the whole system, reproducing the structural variability observed in biological samples. A parametric analysis reveals that the proportion in which each morphology is present in an in silico system is governed mainly by the remodelling characteristic of the fibres, pointing to the essential role of the ECM properties in AT architecture and function (in agreement with several biological results and previous 2D findings). The fact that these very simple mathematical models can produce realistic structures supports our hypothesis that biological tissues architecture could emerge spontaneously from local mechanical inter- actions between the tissue components, independently of the complex biological phenomena taking place around them. This opens many perspectives regarding our understanding of the fundamental principles governing how biological tissue architecture emerges during organogenesis, is maintained throughout life and can be affected by various pathological conditions. Potential applications range from tissue engineering to therapeutic treatment inducing regeneration in adult mammals

Soft fibre networks, typically seen in bathroom tissues, kitchen towels, and personal-care products, have properties that are intricately affected by the details of fibre geometry, 3D-network structures, and processing conditions. Designing such materials and products for better performance, while controlling cost, is especially a challenge in today’s fast paced product development. This paper concerns the development of a new, robust computational design platform for the design of soft fibre networks. We have used particle-based methods, particularly, Discrete Element Method (DEM), to model fibres, fibre networks, their properties and performance, and also unit processes for manufacturing. Unlike other computational methods, this method has advantages to model discrete and non-homogeneous materials, complex geometries, and highly non-linear dynamic problems, such as large deformation (flow), contact/non/contact, fracture, and fragmentation. With this approach, fibres are represented by a series of connected spherical particles in different lengths and geometries (curl, kinks, twists). Fiber networks are created by the deposition of those fibres under gravity, followed by the subsequent consolidation under pressure. here processes have shown an interesting transition phenomenon from a highly fluidic granular system to a fragile soft solid. The network is then subjected to a creping process, a critical process of tissue-making. The model was able, not only to reproduce unique crepe frequencies, but also unprecedented details of the destruction of fibre network structures and fibre failure (dusting) during creping. Typical tensile tests, thickness-direction compression tests, and softness tests have been also performed to demonstrate unique deformation characteristics of low-density, low-basis weight fibre networks. This computational design system based on DEM provides a promising platform for exploring large parameter space of new material/product design.

Kerfing is a subtractive manufacturing approach to create flexible freeform surfaces from stiff planar materials. The kerf structures are used in both indoor and outdoor architectures for wall paneling, outdoor façade and pavilion. In addition to their physical appeal, these structures have potential applications in tuning the dynamics responses in buildings, e.g., indoor acoustic, vibration suppression, etc. To exploit these novel applications of kerf structures, this paper presents a study on the dynamic responses of kerf structures made up of Medium Density Fiberboard (MDF). MDF is a viscoelastic composite material comprising of wood fiber networks and epoxy. The influence of the material behavior, i.e. viscoelasticity of MDF is considered in determining the dynamic response of the kerf panels. Two kerf panels with similar kerfing pattern but different cut density and arrangement are studied for their modal responses. A 3D beam element is used to model the mechanical responses of the kerf panels. With the understanding of the dynamic response of these kerf panels, their applications in altering the indoor acoustics and the wind responses of the buildings can be better comprehended.

Abstract Large, high density fracture networks are necessary to deliver commercial production rates from sub-microdarcy permeability organic-rich shale reservoirs. Operators have increased lateral length and fracture stages as the primary means to improve well performance and, more recently, are tailoring completion techniques to local experience and reservoir-specific learning. In particular, closer fracture stage spacing or increased number of stages per well have driven improvements in well performance. Large scale adoption occurs when the change in performance is clearly linked to the reservoir-specific completion design. Horizontal well fracturing efficiency in unconventional reservoirs is notoriously poor. Numerous authors report that 40 to 60 per cent of frac stages or individual perforation clusters have been shown (albeit with highly uncertain surveillance methods) to contribute little or no production. The fracture initiation and propagation process is very complex in shale; it is affected by in-situ stress, geomechanical heterogeneity, presence of natural fractures, and completion parameters. Close cluster spacing can provide enhanced well production; however, if the spacing is too close, stress shadowing among these clusters can actually induce higher stresses, creating fracture competition. This paper presents an approach to the integration of these parameters through both state-of-the-art geological characterization and unconventional 3D hydraulic fracture modeling. We couple stochastic discrete fracture network (DFN) models of in-situ natural fractures with a state-of-the art 3D unconventional fracture simulator. The modeled fracture geometry and associated conductivity is exported into a dynamic reservoir flow model, for production performance prediction. Calibrated toolkits and workflows, underpinned by integrated surveillance including distributed temperature and acoustic fiber optic sensing (DTS/DAS), are used to optimize horizontal well completions. A case study is presented which demonstrates the technical merits and economic benefits of using this multidisciplinary approach to completion optimization.

Self-healing materials have emerged as an alternative solution to the repair of damage in fibre-reinforced composites. Recent developments have largely focused on a vascular approach, due to the ability to transport healing agents over long distances and continually replenish from an external source. However fracture of the vascular network is required to enable the healing agents to infiltrate the crack plane, ceasing its primary function in transporting fluid and preventing the repair of any further damage events. Here we present a novel approach to vascular self-healing through the development and integration of 3D printed, porous, thermoplastic networks into a thermoset matrix. This concept exploits the inherently low surface chemistry of thermoplastic materials, which results in adhesive failure between the thermoplastic network and thermoset matrix on arrival of a propagating crack, thus exposing the radial pores of the network and allowing the healing agents to flow into the damage site. We investigate the potential of two additive manufacturing techniques, fused deposition modeling (FDM) and stereolithography, to fabricate free-standing, self-healing networks. Furthermore, we assess the interaction of a crack with branched network structures under static indentation in order to establish the feasibility of additive manufacture for multi-dimensional 3D printed self-healing networks.

Abstract Monitoring of CO2 plume migration in a depleted carbonate reservoir is challenging and demand comprehensive and trailblazing monitoring technologies. 4D time-lapse seismic exhibits the migration of CO2 plume within geological storage but in the area affected by gas chimney due to poor signal-to-noise ratio (SNR), uncertainty in identifying and interpretation of CO2 plume gets exaggerated. High resolution 3D vertical seismic profile (VSP) survey using distributed acoustic sensor (DAS) technology fulfil the objective of obtaining the detailed subsurface image which include CO2 plume migration, reservoir architecture, sub-seismic faults and fracture networks as well as the caprock. Integration of quantitative geophysics and dynamic simulation with illumination modelling dignify the capabilities of 3D DAS-VSP for CO2 plume migration monitoring. The storage site has been studied in detailed and an integrated coupled dynamic simulation were performed and results were integrated with seismic forward modeling to demonstrate the CO2 plume migration with in reservoir and its impact on seismic amplitude. 3D VSP illumination modelling was carried out by integrating reservoir and overburden interpretations, acoustic logs and seismic velocity to illustrate the subsurface coverage area at top of reservoir. Several acquisition survey geometries were simulated based on different source carpet size for effective surface source contribution for subsurface illumination and results were analyzed to design the 3D VSP survey for early CO2 plume migration monitoring. The illumination simulation was integrated with dynamic simulation for fullfield CO2 plume migration monitoring with 3D DAS-VSP by incorporating Pseudo wells illumination analysis. Results of integrated coupled dynamic simulation and 4D seismic feasibility were analyzed for selection of best well location to deploy the multi fiber optic sensor system (M-FOSS) technology. Amplitude response of synthetic AVO (amplitude vs offsets) gathers at the top of carbonate reservoir were analyzed for near, mid and far angle stacks with respect to pre-production as well as pre-injection reservoir conditions. Observed promising results of distinguishable 25-30% of CO2 saturation in depleted reservoir from 4D time-lapse seismic envisage the application of 3D DAS-VSP acquisition. The source patch analysis of 3D VSP illumination modelling results indicate that a source carpet of 6km×6km would be cos-effectively sufficient to produce a maximum of approximately 2km in diameter subsurface illumination at the top of the reservoir. The Pseudo wells illumination analysis results show that current planned injection wells would probably able to monitor early CO2 injection but for the fullfield monitoring additional monitoring wells or a hybrid survey of VSP and surface seismic would be required. The integrated modeling approach ensures that 4D Seismic in subsurface CO2 plume monitoring is robust. Monitoring pressure build-ups from 3D DAS-VSP will reduce the associated risks.

We present a novel approach to study the three-dimensional network structure of paper. In the KCL-PAKKA simulation model, the porous sheet structure is compiled from the different papermaking raw materials, fibres, fillers and fines. The model geometry is simplified in order to enable effective numerical experiments with arbitrary composition and layering. The KCL-PAKKA model gives realistic predictions for many paper properties . In this paper we describe the cross-over that occurs with increasing grammage from a thin strictly two-dimensional network to a thick layered networks . According to our simulations the cross-over occurs at low grammages, around 20 – 30 g/m2, for papers made of stifffibres (e.g. mechanical pulp) and at higher grammages, 40 – 80 g/m2, for flexible fibres (e.g. beaten kraft). We discuss the statistical properties of the three-dimensional but layered random fibre network, particularly the bonding degree and pore geometry . In thick networks the pore geometry is isomorphic and only the length scale of the pores depends on fibre properties.

Rotor-dynamical systems made of 3D-fiber-reinforced composites which are subjected to dynamical loads exhibit an increased fiber bending stiffness in numerical simulations. We propose a numerical modeling approach of fiber-reinforced composites that treats this behaviour accurately. Our model uses a multi-field mixed finite element formulation based on a dynamic variational approach, as demonstrated in [1], to perform long-term dynamic simulations that yield numerical solutions with increased accuracy in efficient CPU-time.We extend a Cauchy continuum with higher-order gradients of the deformation mapping as an independent field in the functional formulation, as suggested in [2], to model the bending stiffness of fibers accurately. This extended continuum also takes into account the higher-order energy contributions including the fiber curvature along with popular proven approaches that avoid the numerical locking effect of the fibers efficiently.We apply the proposed approach on Cook’s cantilever beam with a hyperelastic, transversely isotropic, polyconvex material behavior in a transient dynamic analysis. The beam is subjected to bending loads with a strong dependence of the overall stiffness on the fiber orientation. The spatial and temporal convergence as well as the conservation properties are analyzed. It is observed that the model needs an improved numerical treatment to conserve total momenta as well as total energy.REFERENCES M. Groß and J. Dietzsch, "Variational-based locking-free energy–momentum schemes of higher-order for thermo-viscoelastic fiber-reinforced continua", Computer Methods in Applied Mechanics and Engineering, (2019), 631-671, 343. T. Asmanoglo and A. Menzel, “A multi-field finite element approach for the modelling of fibre-reinforced composites with fibre-bending stiffness”, Computer Methods in Applied Mechanics and Engineering, (2017), 1037-1067, 317.

The stress-strain performance of 2D and 3D cellulose fibre networks was simulated using a network model. A model network consists of bonded curled fibres placed at random in a cell. The bonds show a stick-slip fracture behaviour. Results concerning the influence of network density, fibre orientation and ductility of bonds on the stress–strain behaviour of a network are given, and an example of fracture localization is provided.

In the last decades, missions to minor celestial bodies have gained importance, brought forward by missions like Rosetta, OSIRIS-Rex, Hayabusa, or, more recently, DART. These missions are proof that the interest of the space sector in these bodies is growing, and that they are becoming more accessible, aided by the evolution of the technology and autonomous methodologies required. To assist with the analyses needed for the design of these missions, an effort needs to be made to improve the models used for simulations in such environments. Astrodynamics Simulator (AstroSim) is a software tool developed at Deimos Space S.L.U., meant to be used for mission analysis and navigation around small bodies. The tool started as an orbital propagator including the typical central gravity forces and perturbations to be encountered in small-body environments. Gravitational forces can be modelled with many different methodologies, from simpler implementations, such as point-mass modelling, to more complex ones like spherical harmonics or polyhedron-based models. On the other hand, the perturbations included in AstroSim include third-body gravity (unlimited bodies), and Solar Radiation Pressure (SRP). Third body gravity models consider the bodies included as point-masses and take the ephemeris from SPICE, which is internally linked to AstroSim using SPICE’s python module. SRP models use a conical shadowing model that considers umbra, penumbra, and even the rare case of antumbra (annular eclipse) conditions. AstroSim has been recently validated against General Mission Analysis Tool (GMAT), which is a Commercial Off-The-Shelf (COTS) tool used for mission analysis. A very exhaustive campaign was performed, where the different dynamical models were independently tested using 100 random initial states and propagating them for 7 days under different perturbations. Errors after a week of propagation are in the order of magnitude of tenths of centimetres for the worst cases (spherical harmonics, and polyhedron-based models). Additionally, to high-fidelity trajectory propagation, AstroSim offers a wide range of capabilities, such as navigation analysis, Image Processing (IP), image rendering, landing simulation including contact dynamics, Hazard Detection and Avoidance (HDA), or event detection. To integrate these functionalities, different python libraries and third-party software have been used. For instance, image rendering is achieved using Blender and pyrender. The former is an open-use license Video Effects (VFX) suite strongly supported by the community. It offers different features including 3D modelling, UV unwrapping, texturing, raster graphics editing, fluid and smoke simulation, particle simulation, soft body simulation, sculpting, rendering, motion graphics, or compositing, for instance. This makes Blender very attractive for image rendering since it is capable of producing images that resemble what an optical payload would achieve. Pyrender is a Python module capable of rendering images very similarly to Blender, usually much faster. However, Blender offers other advantages that can be directly exploited in a small-body environment simulator. One of them is its contact dynamics engine, which enables rigid-body simulations where collisions and deformations are taken into account, which is exploited in AstroSim to perform contact dynamics simulations for landing sequences. Another of these functionalities is the HDA algorithms included in astroHarm, which are based on Convolutional Neural Networks (CNNs) and use FastAI and PyTorch to build and train the networks needed to conform the three layers developed: shadow detection, feature detection, and slope estimation. These three prediction layers conform a passive system since they take as input only optical observations. Active systems, on the contrary, make use of active instruments, such as Light Detecting And Rangings (LIDARs), for instance, to estimate surface steepness and roughness. Having access to only optical observations makes the slope estimation very challenging, but the networks are capable of predicting hazards (high slopes) presenting accuracies above 70% for true positives. AstroSim was first presented at the AIAA/ASS SciTech 2022 in San Diego (CA), where a preliminary version of the suite was introduced. After that, it was used to produce other conference papers and publications. In this paper, the main functionalities of the tool are described and its architecture is showcased. Firstly, AstroSim is introduced as a modular suite developed in Python, to which different functional blocks can be easily added by exploiting the language’s high versatility and Object-Oriented Programming Language (OOP) style. Then, the results of the validation campaign are included, where the accuracies that the tool is capable of reaching are shown in detail. Examples of the different modules are provided, including astroHarm, a module to obtain the spherical harmonic coefficients from polyhedral shape models; astroHda, an Artificial Intelligence (AI)-based module that uses CNN to detect hazards for a landing simulation (such as shadows, features, or high slopes); or astroRender, a module that uses Blender to render images that can be used for optical navigation and to simulate landings accounting for the contact dynamics of the impact. Finally, the projects and publications to which AstroSim has contributed to are presented, to put into perspective how the tool can be exploited, and conclusions are drawn.