Explicit Simulations Research Articles

Use of multi-modelling methods to inform conservation and reintroductions of pine marten Martes martes in Britain

The successful onset of recovery of the European pine marten (Martes martes) in some parts of Britain through range expansion and, more recently translocation for reintroductions, has resulted in a strong interest in reintroduction projects throughout the country. However, the geographic scope and conservation goals of these initiatives are often local and lack consideration of how they fit within the wider context of national-scale pine marten conservation. Here, we aim to maximize conservation benefit strategically at a national level by developing a simple, transparent, and transferable framework based on landscape modelling methods and spatially explicit population viability analyses. Our new methodology has been developed specifically to inform decisions involving the spatial targeting of pine marten conservation measures. We began by applying habitat suitability and connectivity modelling at a national scale. Then, we performed spatially explicit life history simulations to assess the natural recovery of the species. This information was used to identify regions of interest for future reintroductions, and we performed subsequent simulations to assess the viability of a reintroduced population within each region. From all the regions assessed, we identified two that should be prioritized for further consideration based on our analyses of habitat suitability, connectivity and the viability of reintroduced populations. While our framework can be used to identify and prioritize regions of conservation value generally, our focus here is on the biological considerations associated with identifying suitable landscapes for pine marten reintroduction.

Efficient structural optimization under transient impact loads using multilayer perceptron and genetic algorithms

Currently, there is a lack of structural optimization methods when a structure is suffering transient impact load, considering nonlinear factors such as material plasticity and strain rate effect. The utilization of derivative-free algorithms also increases computational expenses. Accordingly, this paper presents a novel structure transient optimization approach based on multilayer perceptron combined with a derivative-free optimization method, which can efficiently solve the transient structure dynamics optimization problem with multiple optimization objects. In this approach, the area to be optimized is cut by several closed B-spline curves whose shapes are controlled by several parameters and can be varied and merged at each optimization iteration step. The structure's transient analysis process is replaced by a machine learning based surrogate model, which is trained by thousands of results from the FEM explicit transient simulation. Additionally, nearly orthogonal Latin hypercube sampling is utilized to simplify parameter dimensionality, reduce the training data set, save calculation time, and give the training data set a more comprehensive range of design parameters. In our optimization process, our design target is to minimize the peak reaction force while with the constrain of ensuring enough stiffness and mass. The results demonstrate our proposed methods could efficiently handle transient optimization problems without sensitivity calculations, exhibiting strong generalization capabilities.

Verification of electromagnetic simulation capabilities in global gyrokinetic particle-in-cell code GTS

Recently, the numerical scheme presented by Mishchenko et al. [Phys. Plasmas 21, 052113 (2014); 21, 092110 (2014)] enabled explicit gyrokinetic simulations of low-frequency electromagnetic instabilities in tokamaks at experimentally relevant values of plasma β. This scheme resolved the long-standing cancellation problem that previously hindered gyrokinetic particle-in-cell code simulations of magnetohydrodynamic phenomena with inherently small parallel electric fields. Moreover, the scheme did not employ approximations that eliminate critical tearing-type instabilities. Here, we report on the implementation of this numerical scheme in the global gyrokinetic particle-in-cell code GTS. This implementation allows for a more complete and accurate picture of interaction between small scale turbulence and MHD modes in tokamaks. Additionally, we present a comprehensive set of verification simulations of numerous electromagnetic instabilities relevant to present-day tokamaks. These simulations encompass the kinetic ballooning mode, the internal kink mode, the tearing mode, the micro-tearing mode, and the toroidal Alfven eigenmode destabilized by energetic ions, which are all instrumental in understanding tokamak physics. We will also showcase the preliminary nonlinear simulations of kinetic ballooning instabilities and (2,1) island formation due to tearing mode instability. These simulations validate the accuracy of the scheme implementation and pave the way for studying how these instabilities affect plasma confinement and performance.

DEM simulation of an impact crusher using the fast-cutting breakage model

The impact crusher that exploits impact rather than pressure to break down materials has been widely used in the mineral industry. To further improve its performance in material crushing, numerical simulation capable of providing a deeper insight into the complicated dynamics of feed materials than experimental study has emerged as a promising tool. However, the field of the accurate prediction of product quality and explicit simulation of breakage events inside an industrial-scale crusher remains underexplored on account of the absence of a robust breakage model and heavy computational cost. In this study, a numerical investigation of an industrial-scale crusher using the fast-cutting breakage model is conducted within the DEM (Discrete Element Method) framework. The accuracy of the breakage simulation is quantitatively validated by comparing the predicted results with the measurements from actual production. Furthermore, the influence of operating conditions including the rotation speed of the rotor and the feeding rate on the interested parameters, i.e., the power consumption of the equipment, the size distribution of the products, and the impact-induced wear is investigated comprehensively. The results illustrate that (i) the size of the product increases with the rotor speed until a critical value, after which a further increase in rotor speed will exert no significant influence on the size distribution; (ii) The feeding rate is not a critical operating condition that largely determines the fineness of the product and the efficiency of the equipment almost remain stable under a certain range of feeding rates; (iii) The wear of the devices concentrates on the rotor and the impact plates, and the distribution of the wear exhibits different characteristics in different parts, caused by the complex collision between the particles and the devices.

Particle Virtual Element Method (PVEM): an agglomeration technique for mesh optimization in explicit Lagrangian free-surface fluid modelling

Explicit solvers are commonly used for simulating fast dynamic and highly nonlinear engineering problems. However, these solvers are only conditionally stable, requiring very small time-step increments determined by the characteristic length of the smallest, and often most distorted, element in the mesh. In the Lagrangian description of fluid motion, the computational mesh quickly deteriorates. To circumvent this problem, the Particle Finite Element Method (PFEM) creates a new mesh (e.g., through a Delaunay tessellation, based on node positions) when the current one becomes overly distorted. A fast and efficient remeshing technique is therefore of pivotal importance for an effective PFEM implementation in explicit dynamics. Unfortunately, the 3D Delaunay tessellation does not guarantee well-shaped elements, often generating zero- or near-zero-volume elements (slivers), which drastically reduce the stable time-step size. Available mesh optimization techniques have limited applicability due to their high computational cost when runtime remeshing is required. An innovative possibility to overcome this problem is the use of the Virtual Element Method (VEM), a variant of the finite element method that can make use of polyhedral elements of arbitrary shapes and number of nodes. This paper presents the formulation of a 3D first-order Particle Virtual Element Method (PVEM) for weakly compressible flows. Starting from a tetrahedral mesh, poorly shaped elements, such as slivers, are agglomerated to form polyhedral Virtual Elements (VEs) with a controlled characteristic length. This approach ensures full control over the minimum time-step size in explicit dynamics simulations, maintaining stability throughout the entire analysis.

Quantized embedding approaches for collective strong coupling-Connecting abinitio and macroscopic QED to simple models in polaritonics.

Collective light-matter interactions have been used to control chemistry and energy transfer, yet accessible approaches that combine abinitio methodology with large many-body quantum optical systems are missing due to the fast increase in computational cost for explicit simulations. We introduce an accessible abinitio quantum embedding concept for many-body quantum optical systems that allows us to treat the collective coupling of molecular many-body systems effectively in the spirit of macroscopic quantum electrodynamics while keeping the rigor of abinitio quantum chemistry for the molecular structure. Our approach fully includes the quantum fluctuations of the polaritonic field and yet remains much simpler and more intuitive than complex embedding approaches such as dynamical mean-field theory. We illustrate the underlying assumptions by comparison to the Tavis-Cummings model. The intuitive application of the quantized embedding approach and its transparent limitations offer a practical framework for the field of abinitio polaritonic chemistry to describe collective effects in realistic molecular ensembles.

Differentially heterogeneous hydration environment of the familial mutants of α-synuclein.

The behavior of hydration water around familial Parkinson's disease linked mutants of α-synuclein may be linked to the early-onset of Parkinson's disease. For the first time, this study compares the local structure and dynamics of hydration water around different segments of some of the natural mutants of α-synuclein, i.e., E46K, G51D, A30P, and A53E, with that of the wild-type protein through explicit water MD simulations. The results show that the C-terminal segments of the fast aggregating mutants such as E46K and A30P are less exposed to water, while those of the slow aggregating ones such as A53E and G51D are more exposed to water relative to that of the wild-type protein. In addition, the water molecules are found to be more ordered around the C-terminal segment of the A53E and G51D mutants as compared to the wild-type protein. This is due to an increase in the overall charge of α-syn upon A53E and G51D mutations. The translational and rotational motions of water molecules in the hydration shell of the C-terminal segment of A53E and G51D mutants are found to be faster relative to that of the wild-type protein. This study validates the differential hydration environment around the C-terminal segment for the causative and protective mutants of α-synuclein.

Exploring the potential of forest snow modeling at the tree and snowpack layer scale

Abstract. Boreal and sub-alpine forests host seasonal snow for multiple months per year; however, snow regimes in these environments are rapidly changing due to rising temperatures and forest disturbances. Accurate prediction of forest snow dynamics, relevant for ecohydrology, biogeochemistry, cryosphere, and climate sciences, requires process-based models. While snow schemes that track the microstructure of individual snow layers have been proposed for avalanche research, so far, tree-scale processes resolving canopy representations only exist in a few snow-hydrological models. A framework that enables layer- and microstructure-resolving forest snow simulations at the meter scale is lacking to date. To fill this research gap, this study introduces the forest snow modeling framework FSMCRO, which combines two detailed, state-of-the art model components: the canopy representation from the Flexible Snow Model (FSM2) and the snowpack representation of the Crocus ensemble model system (ESCROC). We apply FSMCRO to discontinuous forests at boreal and sub-alpine sites to showcase how tree-scale forest snow processes affect layer-scale snowpack properties. Simulations at contrasting locations reveal marked differences in stratigraphy throughout the winter. These arise due to different prevailing processes at under-canopy versus gap locations and due to variability in snow metamorphism dictated by a spatially variable snowpack energy balance. Ensemble simulations allow us to assess the robustness and uncertainties of simulated stratigraphy. Spatially explicit simulations unravel the dependencies of snowpack properties on canopy structure at a previously unfeasible level of detail. Our findings thus demonstrate how hyper-resolution forest snow simulations can complement observational approaches to improve our understanding of forest snow dynamics, highlighting the potential of such models as research tools in interdisciplinary studies.

Data-Efficient Active Learning for Thermodynamic Integration: Acidity Constants of BiVO4 in Water.

The protonation state of molecules and surfaces is pivotal in various disciplines, including (electro-)catalysis, geochemistry, biochemistry, and pharmaceutics. Accurately and efficiently determining acidity constants is critical yet challenging, particularly when explicitly considering the electronic structure, thermal fluctuations, anharmonic vibrations, and solvation effects. In this research, we employ thermodynamic integration accelerated by com- mittee Neural Network potentials, training a single machine learning model that accurately describes the relevant protonated, deprotonated, and intermediate states. We investigate two deprotonation reactions at the BiVO4 (010)-water interface, a promising candidate for efficient photocatalytic water splitting. Our results illustrate the convergence of the required ensemble averages over simulation time and of the final acidity constant as a function of the Kirkwood coupling parameter. We demonstrate that simulation times on the order of nanoseconds are required for statistical convergence. This time scale is currently unachievable with explicit ab-initio molecular dynamics simulations at the hybrid DFT level of theory. In contrast, our machine learning workflow only requires a few hundred DFT single point calculations for training and testing. Exploiting the extended time scales accessible, we furthermore asses the effect of commonly applied bias potentials. Thus, our study significantly advances calculating free energy differences with ab-initio accuracy.

Distinct Element macro-crack networks for expedited discontinuum seismic analysis of large-scale URM structures

Discontinuum analysis is a powerful tool for the detailed seismic assessment of unreinforced masonry (URM) structures, whose widespread use is however still mostly limited to small-scale assemblies or isolated components. Despite their unique capabilities, including the explicit simulation of separation phenomena, collapses and out-of-plane (OOP) failures that are hardly replicable using traditional continuum solutions, the high computational cost entailed by micro-to-meso-scale discontinuum modelling strategies, which are the standard approaches in applied research, presently prevent their employment for building-scale problems. The few available macro-scale discontinuum models specifically conceived for URM, on the other hand, rely on complex geometrical discretization processes that require costly manual work, as well as on less efficient deformable block formulations. In this paper, a new simplified discontinuum macro-model is presented and validated against full-scale laboratory test outcomes on walls, pier-spandrel systems and building specimens, in addition to various meso-scale numerical results, under either in-plane (IP) or OOP actions, and considering quasi-static (monotonic, cyclic) or dynamic loadings. The proposed simulation technique, implemented in a robust Distinct Element Method (DEM) framework, leverages a semi-automated Equivalent Frame discretization algorithm that idealizes masonry piers, spandrels and nodes as an assembly of interlocked rigid macro-blocks connected by a network of zero-thickness interface springs. The latter are herein demonstrated to effectively replicate URM damage at the component-level through fracture energy contact laws, providing macro-scale yet representative failure patterns, as well as adequate predictions of overall strength and deformation capacities. Results obtained show a good agreement between macro- and more sophisticated meso-scale predictions, as well as with experimental outcomes, albeit dissimilarities among measured and computed force-displacement responses – similarly to other simplified strategies – were detected as vertical overburden increases. Notably, for analogous levels of accuracy, the analysis time required by our new macro-models is up to 150 times lower than its meso-counterparts. The use of the proposed simplified strategy also enabled the satisfactory simulation of the quasi-static cyclic and seismic responses of full-scale building-scale specimens, prohibitive tasks using traditional detailed DEM models, while also being up to 10 times faster than previous deformable macro-models.

Toward the Direct Simulation of the Quasi‐Biennial Oscillation in a Global Storm‐Resolving Model

AbstractThis study presents the first attempt to simulate a full cycle of the quasi‐biennial oscillation (QBO) in a global storm‐resolving model (GSRM) that explicitly simulates deep convection and gravity waves instead of parameterizing them. Using the Icosahedral Nonhydrostatic (ICON) model with horizontal and vertical resolutions of about and , respectively, we show that an untuned state‐of‐the‐art GSRM is already on the verge of simulating a QBO‐like oscillation of the zonal wind in the tropical stratosphere for the right reasons. ICON shows overall good fidelity in simulating the QBO momentum budget and the downward propagation of the QBO jets in the upper QBO domain (25–35 km). In the lowermost stratosphere, however, ICON does not simulate the downward propagation of the QBO jets to the tropopause. This is the result of a pronounced lack of QBO wave forcing, mainly on planetary scales. The lack of planetary‐scale wave forcing in the lowermost stratosphere is caused by an underestimation of planetary‐scale wave momentum fluxes entering the stratosphere. We attribute this lack of planetary‐scale wave momentum fluxes to a substantial lack of convectively coupled equatorial waves (CCEWs) in the tropical troposphere. Therefore, we conclude that in ICON, simulating a realistic spatio‐temporal variability of tropical deep convection, in particular CCEWs, is currently the main roadblock toward simulating a reasonable QBO. To overcome this intermediate situation, we propose to aim at an improved explicit simulation of tropical deep convection by retuning the remaining parameterizations of cloud microphysics and vertical diffusion, and by increasing the horizontal resolution.

Material Selection and Optimization of Green Drilling of Aluminium Alloys: MCDM-Pareto ANOVA and T-SRC

Abstract The widespread use of aluminium alloys in the automotive, aerospace, and structural sectors necessitates the establishment of exploratory selection criteria to harness optimality at the various stages of product development. This study utilizes multi-criteria decision-making approaches to determine the most suitable aluminium alloy for thermal friction drilling. Additionally, the process optimization was done using the Taguchi super ranking concept (T-SRC), and the confirmation test using explicit dynamics simulation using ANSYS software. The ideal material selection was evaluated based on several factors, including yield strength, tensile strength, thermal conductivity, impact resistance, density, specific heat capacity, coefficient of thermal expansion, and percentage elongation. The best-ranked AA2024-T4 aluminium alloy underwent an integrated Taguchi Design of Experiments and optimization using Pareto ANOVA and subsequently ANSYS Explicit Dynamics process simulation. The MCDM Expert System derived from AHP, and ranking of materials using TOPSIS, EDAS, and VIKOR proved to be an effective material selection strategy from a set of alternatives for the thermal friction drilling process. By combining Multi-Criteria decision making (MCDM) with process simulation, material selection concerns in different industrial processes can be resolved, therefore eliminating the need for laborious tests.

A Monte-Carlo approach for crack initiation modeling of cast superalloys informed by crystal plasticity

Crystal plasticity simulations of materials to assess the fatigue response on the microscale are becoming increasingly popular in industrial application. However, in the case of parts made from cast Ni-base superalloys, the amount of pores in a part combined with the fine spatial discretization around the pores needed due to strong mechanical gradients quickly leads to a computationally impractical number of mesh elements. In this paper we show an alternative to explicit crystal plasticity modeling of part-scale porosity by introducing a Monte-Carlo submodel that recombines the fatigue response of single pores predicted by crystal plasticity into the fatigue response of the pore agglomerate. The model can be applied in realtime due to the use of precomputed crystal plasticity results. We demonstrate that fatigue indicator parameters predicted by the Monte-Carlo submodel agree well with those predicted by explicit crystal plasticity simulations. Lastly, we apply the proposed model to study the influence of different porosity volume percentages, pore sizes and pore morphologies on the fatigue indicator parameters of a cast MAR-M247 superalloy.

Implicit Solvent with Explicit Ions Generalized Born Model in Molecular Dynamics: Application to DNA.

The ion atmosphere surrounding highly charged biomolecules, such as nucleic acids, is crucial for their dynamics, structure, and interactions. Here, we develop an approach for the explicit treatment of ions within an implicit solvent framework suitable for atomistic simulations of biomolecules. The proposed implicit solvent/explicit ions model, GBION, is based on a modified generalized Born (GB) model; it includes separate, modified GB terms for solute-ion and ion-ion interactions. The model is implemented in the AMBER package (version 24), and its performance is thoroughly investigated in atomistic molecular dynamics (MD) simulations of double-stranded DNA on a microsecond time scale. The aggregate characteristics of monovalent (Na+ and K+) and trivalent (Cobalt Hexammine, CoHex3+) counterion distributions around double-stranded DNA predicted by the model are in reasonable agreement with the experiment (where available), all-atom explicit water MD simulations, and the expectation from the Manning condensation theory. The radial distributions of monovalent cations around DNA are reasonably close to the ones obtained using the explicit water model: expressed in units of energy, the maximum deviations of local ion concentrations from the explicit solvent reference are within 1 kBT, comparable to the corresponding deviations expected between different established explicit water models. The proposed GBION model is able to simulate DNA fragments in a large volume of solvent with explicit ions with little additional computational overhead compared with the fully implicit GB treatment of ions. Ions simulated using the developed model explore conformational space at least 2 orders of magnitude faster than in the explicit solvent. These advantages allowed us to observe and explore an unexpected "stacking" mode of DNA condensation in the presence of trivalent counterions (CoHex3+) that was revealed by recent experiments.

Spatially explicit simulation and forecasting of urban growth using weights of evidence based cellular automata model in a millennium city of India

The present study focuses on quantifying and simulating the future urban growth based on the land use/land cover (LULC) data created from the Landsat images of the year 1999, 2011, and 2022. These LULC maps help in analysing the expansion of urban areas over the years and forecast their potential growth in the future. The spatio-temporal processes of urban growth are quantified, and future patterns are simulated and forecasted using Weights of Evidence based Cellular Automata model built in Dinamica EGO (Environment for Geoprocessing Objects) platform. The process of urban growth was manifested through prominent contributing factors of infill expansion namely, distance to built-up areas, distance to main roads, population density, and public services etc. The model's performance was evaluated using Kappa statistics and the percentage of correct prediction (PCP) based two-way comparison method. For this purpose, the simulated map was first compared with the observed information of year 2022 using Kappa indices followed by the PCP value (90.40%) exhibiting high predictive ability of the model. These findings corroborate that the model can forecast the future urban growth scenarios effectively with reasonable accuracy. Based on the outcomes, the forecasting of future urban growth scenarios for years 2033 and 2044 was accomplished. Analysis of the LULC changes displays that urban land use will experience the highest increase. Growth in the study area is predicted to increase by 23.5% and 26.7% in year 2033 and 2044 respectively where new urban settlements can appear. The results demonstrated that an integrated geospatial model provides essential information about the pattern, simulation, and prediction of urban growth associated with various driving variables.

Quantum-assisted rendezvous on graphs: explicit algorithms and quantum computer simulations

Abstract We study quantum advantage in one-step rendezvous games on simple graphs analytically, numerically, and using noisy intermediate-scale quantum (NISQ) processors. Our protocols realise the recently discovered (Mironowicz 2023 New J. Phys. 25 013023) optimal bounds for small cycle graphs and cubic graphs. In the case of cycle graphs, we generalise the protocols to arbitrary graph size. The NISQ processor experiments realise the expected quantum advantage with high accuracy for rendezvous on the complete graph K 3. In contrast, for the graph 2 K 4 , formed by two disconnected 4-vertex complete graphs, the performance of the NISQ hardware is sub-classical, consistent with the deeper circuit and known qubit decoherence and gate error rates.

FPGA-Based Implicit–Explicit Real-Time Simulation Solver for Railway Wireless Power Transfer With Nonlinear Magnetic Coupling Components

FPGA-Based Implicit–Explicit Real-Time Simulation Solver for Railway Wireless Power Transfer With Nonlinear Magnetic Coupling Components

Dynamic Recrystallization of Olivine During Simple Shear: Evolution of Microstructure and Crystallographic Preferred Orientation From Full‐Field Numerical Simulations

AbstractUpper mantle deformation is mainly controlled by the mechanical behavior of olivine. Crystallographic preferred orientations (CPOs) develop in olivine due to crystal‐plastic deformation during mantle flow, where the a‐axes of olivine polycrystalline aggregates are aligned with the flow direction. Therefore, the observed CPO in olivine‐rich rocks is used as an indicator of the mantle flow direction. Experimental data show that olivine rheology is strongly controlled by the microstructure. While the influence of plastic deformation is in general well characterized, the role of dynamic recrystallization during deformation is not totally understood, limiting our ability to interpret the deformation history of naturally deformed rocks. This contribution presents microdynamic numerical simulations of olivine polycrystalline aggregates with different iron content (i.e., fayalite content) with the aim of exploring the CPO and grain size response to dynamic recrystallization. We use a full‐field approach with an explicit simulation of viscoplastic deformation and dynamic recrystallization processes under simple shear boundary conditions up to high strain. The simulations show that the CPOs are similar and practically reach the same maximum regardless of the iron content. CPOs are characterized by a single cluster of a‐axis and two‐clusters of b‐axis, reveling a joint activity of the easy glide [100](010) and the moderate strength [100](010) slip systems. High‐strain domains of our models are consistent with experimental results, showing an A‐type fabric with double maxima, and where the CPO is aligned with the shear direction. The model provides a deeper understanding of the dynamic recrystallization influence on olivine CPOs resulting from plastic deformation.

Impact resistance and energy absorption characteristics of nickel-based alloy ring at elevated temperatures

To evaluate the high-temperature containment capability of a turbine casing, the impact resistance of half-ring specimens and full circular casings made of GH4169 nickel-based alloy was investigated. Ballistic impact tests were first conducted on GH4169 half-ring specimens using blade-shaped projectiles of DZ125 material fired from a single-stage gas gun. Meanwhile, explicit finite element simulations of the half-ring impact tests were performed with LS-DYNA. Comparing experimental and simulation results validated the accuracy of the model and revealed the effects of impact angle and ambient temperature. Then the process of a turbine casing containing a blade at 500 °C was simulated using the validated FE model. The results showed that the impact angle and the ambient temperature significantly affect the impact resistance of GH4169 half-ring specimen. Furthermore, the interaction mechanism between the turbine blade and casing differs across the three stages of the containment process.

UAV Wing leading edge crashworthiness behaviour under bird strike events: The added value of CF/PA additive solutions versus traditional metallic wing structures

In recent years, an increasing interest in innovative solutions design of aircraft structural components has been raised through both research and industrial fields, aimed at optimising weight and enhancing the ability to withstand both static and dynamic loads. This study compares the structural response to a bird strike phenomenon of a vertical tail of a UAV in standard metallic configuration with the one obtained from an innovative solution, equal in volume but with an internally designed architecture for an additive approach and manufactured by employing a carbon fibre reinforced filament engineered for metal replacement applications (carbon fibre, CF/polyamide, PA). The additive solution proposes the use of a 10 % infill and a lattice structure that completely replaces the traditional aircraft structure concept. This approach leads to a significant weight reduction, approximately 45 % compared to the traditional metallic configuration. The investigation was conducted through explicit numerical simulations considering different impact angles. The numerical model of the bird strike has been assessed by numerical-experimental comparison, simulating the impact of a bird with a flat plate. For this study, the Coupled Eulerian-Lagrangian (CEL) approach has been adopted to perform the simulation. The results were compared in terms of stress distribution, failure analysis, displacements, and energy-time and force-time diagrams. The work demonstrated that using innovative manufacturing processes, such as additive manufacturing, can significantly improve the bird strike resistance of aerospace structures. This improvement is achieved though the production of lighter, structurally collaborative geometries, by reducing the load transferred to the rest of the UAV by about 47 % and decreasing the displacement on the impact area by 53 %.