Multiscale Modeling Technique Research Articles

A Multi-Scale Approach for Modelling Airborne Transport of Mucosalivary Fluid

COVID-19 pandemic promoted a lot of research activities in relation to mucosalivary fluid airborne transport. Indeed, infection mechanisms are the result of mucosalivary fluid droplets exchange and the knowledge in this area is still largely inadequate. One of the main challenges concerns the modelling of mucosalivary fluid complex nature. Specifically, this is a key element to predict small diameters dry nuclei formation which are highly relevant from the transmission risk point of view. For this reason, in this paper we present and discuss the development of a new multi-scale modelling technique which incorporates the Population Balance Equation into a standard particle-source-in-cell method. Thus, the effectiveness of the aforementioned technique in droplet nuclei generation modelling is showed and discussed. Also the impact of velocity boundary conditions at the mouth print is assessed as well as the effect of the correlations for mass transfer showing that their neglect causes an underestimation in distance reached by the droplets.

Multiscale analysis of the effect of interfacial thermal conductance between fillers and epoxy resin on the effective thermal conductivity of their composites

The heat dissipation performance of the epoxy molding compound(EMC) is very important for high power application. In this paper, the thermal transport properties of thermoset epoxy(EP) based EMC containing silica particles(SiO2Ps) and diamond particles(CPs) were calculated using two different scale approaches. Firstly, the representative volume element(RVE) of EMC was modeled using molecular dynamics(MD) to calculate the effective thermal conductivity(ETC). The crosslinking of EP between Diglycidyl Ether of Bisphenol A(DGEBA) resin and JEFFAMINE® D-230 hardener was modeled. The results indicate that the CPs can increase the ETC of composite by 71.4 % than that of SiO2Ps. The interfacial thermal conductance(ITC) of Silica/EP interface was 1.33 times higher than that of Diamond/EP interface. The discontinuous temperature gradients on interface was observed in the RVE filled with dispersed fillers. In addition, a macroscale approach was used to determine the ETC of the EMC using finite element method(FEM) and the Maxwell theoretical model. The effects of fillers' shape and the ITC of fillers/EP interface on the ETC of EMC were analyzed. With the increase of the ITC, the ETC of the composite increases gradually. When the ITC exceeded a critical value, the ETC did not change. The chain fillers were more conducive to heat transport. The results of the two multiscale modeling techniques were well consistent.

A genetic evolved machine learning approach for 3D DEM modelling of anisotropic materials with full consideration of particulate interactions

Rapid development of multiscale modelling techniques has enabled significant improvement in understanding material failure. However, accurate simulation of general anisotropic materials still remains a great challenge. This is due to the unbalanced number of material parameters required by models of different scales, and it is difficult and sometime impossible to extract micro material properties from known macro properties. This paper proposes a new 3D discrete element model (DEM) to take full interactions between material particles for general anisotropic composite materials. The challenging issue in determining the micro bond properties of the 3 DEM model is resolved by coupling machine learning (ML) technique with the genetic algorithm (GA). The learned bond properties are validated by comparing DEM predicted macro material properties with experimental results. The micro bond properties are further used to predict strength and simulate crack patterns of bolted composite lap joints. The predictions of the ML model agree well with experimental results of the joints.

Dual continuum upscaling of liver lobule flow and metabolism to the full organ scale

The liver is the body’s primary metabolic organ and its functions operate at multiple time and spatial scales. Here we employ multiscale modelling techniques to describe these functions consistently, based on methods originally developed to describe reactive fluid flow processes in naturally-fractured geological sediments. Using a fully discretized idealized lobule model for flow and metabolism, a dual continuum approach is developed in two steps: 1) Two interacting continua models for tissue and sinusoids properties, followed by 2) further upscaled dual continua models leading to an averaged lobule representation. Results (flows, pressures, concentrations, and reactions) from these two approaches are compared with our original model, indicating the equivalences and approximations obtained from this upscaling for flow, diffusion, and reaction parameters. Next, we have generated a gridded dual continuum model of the full liver utilizing an innovative technique, based on published liver outline and vasculature employing a vasculature generation algorithm. The inlet and outlet vasculature systems were grouped into five generations each based on radius size. With a chosen grid size of 1 mm3, our resulting discretized model contains 3,291,430 active grid cells. Of these cells, a fraction is occupied vasculature, while the dominant remaining fraction of grid cells approximates liver lobules. Here the largest generations of vasculature occupy multiple grid cells in cross section and length. The lobule grid cells are represented as a dual continuum of sinusoid vasculature and tissue. This represents the simplest gridded dual continuum representation of the full liver organ. With this basic model, numerous full liver drug metabolism simulations were run. A non-reactive PAC (paclitaxel) injection case including only convective transfer between vasculature and tissue was compared with including an additional diffusive transfer mechanism. These two cases were then rerun with tissue reaction, converting injected PAC to PAC-OH (6-hydroxypaclitaxel). There was little transfer of PAC from vasculature to tissue without the addition of diffusive transfer, and this had a significant observable effect on internal PAC distribution in the absence of reaction, and also on the distribution of PAC-OH for the reactive cases.

Atomistic to continuum simulations of fracture and damage evolutions in oxide glass and glass-ceramic materials: A critical review

Fracture and damage ascribed to the intrinsic brittleness of amorphous oxide glasses are crucial problems for the daily use of glass products. Because the latest developments in glass and glass-ceramics technologies have further broadened their applications, the safety issues become increasingly important. Computational modeling and simulation are now indispensable in the design and analysis of glass quality and safety. This review, therefore, provides an overview of the state-of-the-art fracture modeling/simulation techniques ranging from atomistic scale to continuum scale. In addition to the fundamental theories, typical and recent studies using a variety of continuum methods are introduced. This review also covers the application examples of classical molecular dynamics (CMD) simulations and reactive CMD simulations to investigate the fracture and damage evolutions in glass and glass-ceramics. Advanced multiscale modeling techniques that bridge atomistic and continuum method are also introduced for modeling amorphous materials.

Coupled diffusion-mechanics framework for simulating hydrogen assisted deformation and failure behavior of metals

Understanding of years-old multifaceted hydrogen-assisted damage evolution necessitates efforts to model the coupled diffusion-mechanics response in metallic materials. Informed by the dislocation-hydrogen interactions, understood earlier via experiments and/or multi-scale modeling techniques, this work presents a dislocation density-based crystal plasticity model coupled with a hydrogen diffusion/trapping model to simulate the hydrogen-assisted deformation and failure under the HELP mechanism of hydrogen embrittlement. The important role of hydrogen on dislocation multiplication, annihilation and dislocation interaction weakening are included in the presented framework. Two possible scenarios under HELP mechanism, leading to H-induced macroscopic softening or hardening as a result of trade-off between the hydrogen-induced weakening of dislocation interactions and hydrogen-induced increased dislocation density emerges from the simulation studies. These finding points towards the inevitable role of HELP mechanism to cause early failure in metals either working independently or in support with additional mechanisms.

Heterogeneous solid element for multiscale modeling of aperiodic composite structures

Heterogeneous Solid Element (HSE) is developed based on Mechanics of Structure Genome (MSG) to model a heterogeneous body as an equivalent solid element by an effective element stiffness matrix. HSE modeling includes homogenization, macroscopic global analysis, and dehomogenization to recover local strain/stress fields. HSE avoids the local periodicity assumption for traditional multiscale modeling techniques for composite structures which compute effective material properties instead. Abaqus composite solid element and MSG-based traditional multiscale modeling are used to validate the accuracy of HSE. All example results show that HSE is more accurate in predicting global structural behavior and local strains/stresses. HSE provides a novel concept for multiscale modeling by homogenizing heterogeneous microstructures into equivalent solid elements rather than material points.

Digital Synthesis of Realistically Clustered Carbon Nanotubes

A computational approach for creating realistically structured carbon nanotubes is presented to enable more accurate and impactful multi-scale modeling and simulation techniques for nanotube research. Much of the published literature to date involving computational modeling of carbon nanotubes simplifies their structure as being long and straight, and often existing as isolated individual nanotubes. However, imagery of nanotubes has shown over several decades that nanotubes agglomerate together and exhibit looping and curvature due both to inter- and intra-nanotube attraction. The research presented in this paper leverages multi-scale simulations consisting of a simple bead-spring model for initial nanotube relaxation followed by a differential geometry approach to create an atomic representation of carbon nanotubes, and then finalized with molecular dynamics simulations using the Tersoff potential model for carbon that allows dynamic bonding and cleavage. The result is atomically accurate representations of carbon nanotubes that exist as single nanotubes, or as clusters of multiple nanotubes. The presented approach is demonstrated using (5,5) single-walled carbon nanotubes. The synthesized nanotubes are shown to relax into the curving and looping structures observed in transmission or scanning electron microscopy, but also exhibit nano-scale defects due to buckling, crimping, and twisting that are resolved during the molecular dynamics simulations. These features locally compromise the desired strength characteristics of nanotubes and therefore the presented procedure will enable more accurate modeling and simulation of nanotubes in subsequent research by representing them less as the theoretically straight and independent entities, but as realistically imperfect.

Modeling Polyzwitterion-Based Drug Delivery Platforms: A Perspective of the Current State-of-the-Art and Beyond.

Drug delivery platforms are anticipated to have biocompatible and bioinert surfaces. PEGylation of drug carriers is the most approved method since it improves water solubility and colloid stability and decreases the drug vehicles’ interactions with blood components. Although this approach extends their biocompatibility, biorecognition mechanisms prevent them from biodistribution and thus efficient drug transfer. Recent studies have shown (poly)zwitterions to be alternatives for PEG with superior biocompatibility. (Poly)zwitterions are super hydrophilic, mainly stimuli-responsive, easy to functionalize and they display an extremely low protein adsorption and long biodistribution time. These unique characteristics make them already promising candidates as drug delivery carriers. Furthermore, since they have highly dense charged groups with opposite signs, (poly)zwitterions are intensely hydrated under physiological conditions. This exceptional hydration potential makes them ideal for the design of therapeutic vehicles with antifouling capability, i.e., preventing undesired sorption of biologics from the human body in the drug delivery vehicle. Therefore, (poly)zwitterionic materials have been broadly applied in stimuli-responsive “intelligent” drug delivery systems as well as tumor-targeting carriers because of their excellent biocompatibility, low cytotoxicity, insignificant immunogenicity, high stability, and long circulation time. To tailor (poly)zwitterionic drug vehicles, an interpretation of the structural and stimuli-responsive behavior of this type of polymer is essential. To this end, a direct study of molecular-level interactions, orientations, configurations, and physicochemical properties of (poly)zwitterions is required, which can be achieved via molecular modeling, which has become an influential tool for discovering new materials and understanding diverse material phenomena. As the essential bridge between science and engineering, molecular simulations enable the fundamental understanding of the encapsulation and release behavior of intelligent drug-loaded (poly)zwitterion nanoparticles and can help us to systematically design their next generations. When combined with experiments, modeling can make quantitative predictions. This perspective article aims to illustrate key recent developments in (poly)zwitterion-based drug delivery systems. We summarize how to use predictive multiscale molecular modeling techniques to successfully boost the development of intelligent multifunctional (poly)zwitterions-based systems.

Electronic Transport in 2D‐Based Printed FETs from a Multiscale Perspective (Adv. Electron. Mater. 5/2022)

Inkjet-Printed Devices A novel multi-scale modeling technique to study transport in 2D materials inkjet-printed devices is presented in article number 2100972 by Marta Perucchini, Damiano Marian, Gianluca Fiori, and co-workers. The approach is aimed at reducing the gap between theory and experiments, being able to rationalize the behavior of fabricated 2D material printed structures, as well as to be useful as a guide for future experimental activity in the field.

Microstructural analysis and multiscale modeling for stiffening and strengthening of consolidated earthen-site soils

While experimental explorations to consolidate earthen-site soils using composite materials have obtained desirable reinforcing effect, few modeling efforts were conducted to reveal how microstructures of consolidated soils affect their effective stiffness and strength. The current study develops a multiscale modeling technique to quantify the stiffening and strengthening effects of consolidants to earthen-site soils. Specifically, a micromechanics-based finite element method is applied to simulate the homogenized constitutive relations of the untreated and consolidated soils, based on the microstructures from scanning electron microscopy images and image-processing procedures. Multiscale models are used to examine the influence of input properties of consolidants on the sensitivity of mechanical properties of the earthen-site samples. Comparison between modeling and experimental results shows a satisfactory agreement. The macroscopic mechanical properties of soils are remarkably affected by their microstructures. This study demonstrates the feasibility of integrating scanning electron microscopy and image processing technology into the finite-element modeling for stiffening and strengthening analysis of consolidated earthen-site soils.

Descriptive multiscale modeling in data-driven neuroscience

Multiscale modeling techniques have attracted increasing attention by philosophers of science, but the resulting discussions have almost exclusively focused on issues surrounding explanation (e.g., reduction and emergence). In this paper, I argue that besides explanation, multiscale techniques can serve important exploratory functions when scientists model systems whose organization at different scales is ill-understood. My account distinguishes explanatory and descriptive multiscale modeling based on which epistemic goal scientists aim to achieve when using multiscale techniques. In explanatory multiscale modeling, scientists use multiscale techniques to select information that is relevant to explain a particular type of behavior of the target system. In descriptive multiscale modeling scientists use multiscale techniques to explore lower-scale features which could be explanatorily relevant to many different types of behavior, and to determine which features of a target system an upper-scale data pattern could refer to. Using multiscale models from data-driven neuroscience as a case study, I argue that descriptive multiscale models have an exploratory function because they are a sources of potential explanations and serve as tools to reassess our conception of the target system.

Electronic Transport in 2D‐Based Printed FETs from a Multiscale Perspective

AbstractAs 2D materials (2DMs) gain the research limelight as a technological option for obtaining on‐demand printed low‐cost integrated circuits with reduced environmental impact, theoretical methods able to provide the necessary fabrication guidelines acquire fundamental importance. Here, a multiscale modeling technique is exploited to study electronic transport in devices consisting of a printed 2DM network of flakes. The approach implements a Monte Carlo scheme to generate the flake distribution. By means of ab initio density functional theory calculations together with non equilibrium Green's functions formalism, detailed physical insights on flake‐to‐flake transport mechanisms are provided. This later feeds a 3D drift‐diffusion and Poisson solution to compute self‐consistently transport and electrostatics in the device. The method is applied to MoS2 and graphene‐based dielectrically gated FETs, highlighting the impact of the structure density and variability on the mobility and sheet resistance. The prediction capability of the proposed approach is validated against electrical measurements of in‐house printed graphene conductive lines as a function of film thickness, demonstrating its strong potential as a guide for future experimental activity in the field.

Multiscale modelling techniques in Life Cycle Assessment: application to product design

This paper aims at broadening the perspective in life cycle assessment methodology, exploiting multiscale modelling towards the generation of life cycle inventory data during an early-stage product design. Our approach involves the usage of molecular modelling techniques, such as electronic, atomistic or mesoscale models, in combination with continuum models, such as process simulation or finite element methods, to provide data for the generation of life cycle inventories of complex materials and production processes. In particular, each simulation is performed at a specific length and time scale through dedicated software, passing information from the lower to the upper scale.A practical application of the proposed approach will be illustrated for property predictions of nanostructured polymer systems used for manufacturing a marine engine cover. The material formulations capable of fulfilling the marine thermomechanical constraints were defined using in-silico techniques, allowing for the generation of materials life cycle inventories, which have been compared to identify the most environmentally friendly option.

Co-doping studies to enhance the life and electro-chemo-mechanical properties of the LiMn2O4 cathode using multi-scale modeling and neuro-computing techniques.

A number of engineered cathode materials with longer life cycles and better electro-chemo-mechanical properties can be obtained by partially replacing some of the elements with other relevant ones without compromising much with the structure. To design such superior cathode materials, in this work, we replace a small number (5% or 10%) of Mn3+, with one of the following elements: aluminium, nickel, magnesium, gallium, chromium, and yttrium. Additionally, S2- and F- were used to replace some (∼1%) of the O2- ions (anion) in the crystal. In this work, we have used a combination of Quantum Mechanics (QM), Classical Molecular Dynamics (CMD), Neural Network (NN) and Computational Fluid Dynamics (CFD) modeling. QM has been used to validate the Classical Molecular Dynamics (CMD) simulation results for engineered structures where experimental data are not available. CMD simulations are used to obtain material properties such as lattice expansion, Young's modulus, and diffusion coefficients for un-doped, doped and co-doped structures. NN modeling was used to reduce the computational time to evaluate millions of possible crystal configurations. Finally, the impact of co-doping strategies at the macroscale has been studied using CFD simulations. As a first step, we employed neuro-computing techniques to identify the optimum ionic configuration for all crystal structures, saving ∼88% of the computational time. Next, molecular scale simulations were performed to study the material properties. Molecular dynamics (MD) modeling findings suggest that the relative volume expansion between the fully charged and discharged states of the battery can be reduced by ∼1.9% to ∼2.25%, indicating an improvement in the life of the cathode material by several hundreds of cycles. Findings from both QM and CMD simulations suggest that for these novel engineered materials, electro-chemo-mechanical properties, such as ionic mobility, chemical diffusion coefficient and elasticity, improved. Furthermore, CMD simulations showed that the inter-ionic space between doped metal ions and oxygen is smaller compared to the spacing between Mn3+-O2- in the original LMO spinel, indicating an improvement in the material's structural strength along with the total number of the discharge cycle. Finally, macro scale computational modelling results show that chances of thermal runaway can be reduced significantly for some of the co-doped structures since the intercalation induced maximum stress is lower.

A Conserved Allosteric Site on Drug-Metabolizing CYPs: A Systematic Computational Assessment.

Cytochrome P450 enzymes (CYPs) are the largest group of enzymes involved in human drug metabolism. Ligand tunnels connect their active site buried at the core of the membrane-anchored protein to the surrounding solvent environment. Recently, evidence of a superficial allosteric site, here denoted as hotspot 1 (H1), involved in the regulation of ligand access in a soluble prokaryotic CYP emerged. Here, we applied multi-scale computational modeling techniques to study the conservation and functionality of this allosteric site in the nine most relevant mammalian CYPs responsible for approximately 70% of drug metabolism. In total, we systematically analyzed over 44 s of trajectories from conventional MD, cosolvent MD, and metadynamics simulations. Our bioinformatic analysis and simulations with organic probe molecules revealed the site to be well conserved in the CYP2 family with the exception of CYP2E1. In the presence of a ligand bound to the H1 site, we could observe an enlargement of a ligand tunnel in several members of the CYP2 family. Further, we could detect the facilitation of ligand translocation by H1 interactions with statistical significance in CYP2C8 and CYP2D6, even though all other enzymes except for CYP2C19, CYP2E1, and CYP3A4 presented a similar trend. As the detailed comprehension of ligand access and egress phenomena remains one of the most relevant challenges in the field, this work contributes to its elucidation and ultimately helps in estimating the selectivity of metabolic transformations using computational techniques.

Exhibiting environment sensitive optical properties through multiscale modelling: A study of photoactivatable probes

To assess a tumor biomarker like the cyclooxygenase-2 enzyme (COX-2), non-invasive imaging techniques are powerful tools. The (non-) linear optical properties of activatable fluorescent probes which are selectively bound to the biomarker can therefore be exploited. The here presented molecular modelling results based on multi-scale modelling techniques highlight the importance of the conformational versatility and of changes in the electronic interactions of such probes when they are embedded in water or in the COX-2 homodimer enzyme. The ANQ-IMC-6 probe, which combines the binding domain/scaffold of indomethacin (IMC) on COX-2 with the optical properties of acenaphtho[1,2-b]quinoxaline (ANQ), is found to be folded in the solvent and unfolded in the enzyme. A concerted movement of the probe and the protein is seen, while the rotational autocorrelation function exhibits also the intrinsic properties of the probe. Hybrid Quantum Mechanics/Molecular Mechanics (QM/MM) calculations are used to simulate the one-photon and two-photon absorption spectra along with the first hyperpolarizability. The transition has a local character in vacuum, but changes to a charge transfer one in the presence of the microenvironment of the enzyme. This is also visible through a change of the shape of the absorption spectrum, while at the same time the simulated signals of second harmonic generation experiments are strongly enhanced. The results of this work prove that an environment sensitive probe with an anchoring group and an optical active part can be constructed for use in absorption spectroscopy, without the need to revert to fluorescence experiments.

Two modified multiscale modeling approaches for determination of two-phase and hybrid nanocomposite properties

In this paper, mechanical properties of thermoset epoxy based two-phase and hybrid nanocomposites containing carbon nanotubes (CNTs), and carbon nano diamond particles (CNPs) are determined using two different multiscale modeling approaches. The effects of resin crosslinking, interphase mechanical properties, and filler agglomeration on nanocomposite mechanical properties are investigated. First, the crosslinking between Diglycidyl ether of bisphenol A (DGEBA) resin and Diethylenetriamine (DETA) hardener is modeled considering different crosslinking forms and ratios using molecular dynamics (MD) simulations. Results indicate that resin elastic modulus increases with increasing the crosslinking ratio especially above 75%, but crosslinking form has an insignificant effect on resin modulus. Next, different nanofillers and their interphases are modeled using MD simulations to determine the representative volume element (RVE) and the effective fiber sizes and mechanical properties. The thickness of the interphase for each nano filler type is determined from the radial distribution function (RDF) diagram in order to determine the effective fiber volume more realistically. Then, the general Halpin-Tsai model is modified by adding an interphase volume factor and is used to determine two-phase and hybrid nanocomposite mechanical properties using effective nano filler properties. In addition, a new numerical multiscale modeling technique was developed which uses the MD-determined effective filler properties along with finite element method (FEM) to determine nanocomposite mechanical properties. Nanocomposites reinforced with aligned and randomly oriented reinforcements are modeled. Good agreement is observed between the two multiscale modeling techniques for the two-phase nanocomposite mechanical properties. Finally, the effect of filler agglomeration on nanocomposite properties is investigated. The results indicate that agglomeration decreases elastic modulus of CNT/epoxy nanocomposite. However, agglomeration does not have a significant effect on elastic modulus of CNP/epoxy nanocomposite.

Lactation alters fluid flow and solute transport in maternal skeleton: A multiscale modeling study on the effects of microstructural changes and loading frequency.

The female skeleton undergoes significant material and ultrastructural changes to meet high calcium demands during reproduction and lactation. Through the peri-lacunar/canalicular remodeling (PLR), osteocytes actively resorb surrounding matrix and enlarge their lacunae and canaliculi during lactation, which are quickly reversed after weaning. How these changes alter the physicochemical environment of osteocytes, the most abundant and primary mechanosensing cells in bone, are not well understood. In this study, we developed a multiscale poroelastic modeling technique to investigate lactation-induced changes in stress, fluid pressurization, fluid flow, and solute transport across multiple length scales (whole bone, porous midshaft cortex, lacunar-canalicular pore system (LCS), and pericellular matrix (PCM) around osteocytes) in murine tibiae subjected to axial compression at 3N peak load (~320με) at 0.5, 2, or 4Hz. Based on previously reported skeletal anatomical measurements from lactating and nulliparous mice, our models demonstrated that loading frequency, LCS porosity, and PCM density were major determinants of fluid and solute flows responsible for osteocyte mechanosensing, cell-cell signaling, and metabolism. When loaded at 0.5Hz, lactation-induced LCS expansion and potential PCM reduction promoted solute transport and osteocyte mechanosensing via primary cilia, but suppressed mechanosensing via fluid shear and/or drag force on the cell membrane. Interestingly, loading at 2 or 4Hz was found to overcome the mechanosensing deficits observed at 0.5Hz and these counter effects became more pronounced at 4Hz and with sparser PCM in the lactating bone. Synergistically, higher loading frequency (2, 4Hz) and sparser PCM enhanced flow-mediated mechanosensing and diffusion/convection of nutrients and signaling molecules for osteocytes. In summary, lactation-induced structural changes alter the local environment of osteocytes in ways that favor metabolism, mechanosensing, and post-weaning recovery of maternal bone. Thus, osteocytes play a role in balancing the metabolic and mechanical functions of female skeleton during reproduction and lactation.

Multiscale modelling techniques in life cycle assessment: Application to nanostructured polymer systems in the maritime industry

One of the major challenges of Life Cycle Assessment is in the generation of life cycle inventories for complex or novel materials. This paper aims at broadening the perspective in Life Cycle Assessment methodology, exploiting multiscale modelling towards the generation of inventory data during an early-stage product design. Our approach involves the usage of molecular modelling techniques, such as electronic, atomistic or mesoscale models, in combination with continuum models, such as process simulation or finite element methods, to provide data for the generation of life cycle inventories of nanostructured materials. In particular, each simulation is performed at a specific length and time scale through specific software, passing information from the lower to the upper scale. We applied our procedure to a comparative Life Cycle Assessment among suitable materials for the production of a set of cylinder head cover of a marine engine, i.e., aluminium alloys and nano-engineered thermoplastic polymers. Engineering judgment, Ashby charts and maritime regulations led the initial identification of suitable materials. As a result, polyamide 6,6 reinforced with 30% wt. glass fibers and mixed with phosphate-based flame retardants have been identified as promising candidate for the replacement of aluminium traditional covers. Nonetheless, we introduced nanoclays, i.e., exfoliated montmorillonite, as a replacement for a quota of conventional flame retardant, aiming at reducing the usage of pollutant chemicals. We then resorted to atomistic and finite-element techniques to investigate the compliance of nanocomposite materials with several Key Performance Indicators. The nanocomposite formulations obtained from simulations have been finally employed as input data for life cycle inventories of material alternatives. Using a Life Cycle Assessment approach, we could identify the most environmental-friendly nanocomposite formulation, which was then compared with traditional aluminium through an additional comparative cradle-to-grave study. Among the suitable nanocomposite formulations, the material with the minimum amount of traditional flame retardant exhibited the lowest scores among the majority of Environmental Footprint impact categories. To account for the entire lifetime of the marine engine, a set of four nanocomposite covers has been compared with a set of three aluminium covers made by two different metal alloys. Four recycling scenarios for aluminium covers have been examined, assuming an incremental number of end-of-use products reintroduced into a circular life cycle, while nanocomposite ones were disposed into landfill. In turn, if the aluminium products are insufficiently recycled, our results promote the usage of nano-engineered polymer products. As the amount of reused metal covers increases, indeed, polymer-based covers become unfavorable with respect to aluminium cast alloy products. Accordingly, an increasing amount of post-consumer aluminium diminishes the need of primary metal, yielding considerable advantages in terms of environmental footprint. Consequently, stakeholders should commit in recycling metal components not only at ship dismantling, but also during ordinary maintenance. Our study showed how multiscale molecular modelling proved to be useful for providing necessary inventory data for complex nanostructured materials. This preliminary study paves the way for future development of the proposed framework, in which in-silico techniques based on distinct paradigms can mutually contribute towards an improvement of products ecodesign.