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This paper proposes a multi-fidelity and multi-disciplinary framework that combines low- and high-fidelity aerothermodynamics, thermal analysis, flight dynamics, and structural analysis in a modular approach to achieve a favourable trade-off between cost and accuracy. The novelty in the current study is two-fold: one is to simulate a more accurate destructive re-entry process over using a prescribed altitude trigger for fragmentation, and the other is to implement automatic fidelity switches between high- and low-fidelity models for the aerothermodynamics based on the shock-envelope approximation of Billig's formulation. For the high-fidelity flow modelling, the open-source SU2-NEMO code is used to solve the slip to continuum regimes while the SPARTA-DSMC solver is used for transitional and free-molecular regimes. To estimate the fragmentation altitude, a linear structural analysis of objects modelled as joints are continually carried out using the FEniCS finite elements solver. A temperature-dependent von Mises yield criterion is used to identify failure in joints. The software framework, TITAN Transatmospheric Flight Simulation, is applied to the ESA ATV re-entry and fragmentation test case.

This user guide outlines how to use the Web Feature Service (WFS) through third-party software, specifically QGIS and/or R software environment as these software solutions are free and open-source (https://qgis.org, https://www.r-project.org/). For an overview of the OCTOPUS database, see: https://epicaustralia.org.au/resource/octopus https://www.re3data.org/repository/r3d100013702 For detailed information about the OCTOPUS database structure, data collections, and documentation, see: https://zenodo.org/communities/octopus-database/

This paper provides a systematic approach to study the influence of electrical steel grade on electric vehicle traction motor performance. A permanent magnet assisted synchronous reluctance motor, an interior permanent magnet motor and an induction motor are considered to systematically quantify the influence of lamination thicknesses from 0.1 to 0.35mm, and materials with 3% and 6.5% silicon contents. Laminations with low silicon steel are better candidates in terms of peak and continuous torque production for low-speed operations. 0.1mm 6.5% silicon steel - 10JNEX900 can achieve higher efficiency and better flux-weakening capability above 400 Hz. Its higher mechanical strength is beneficial for complex rotor structure with bridges under stress from centrifugal forces. Therefore, grade choice relies on a compromise between low and high speed performance, propulsion system cost estimation and manufacturing challenges. Application related priorities define which grade is best suited.

In this paper, an Advanced Dynamic Model Predictive Control (AMPC) based on a Nonlinear Model Predictive Control (NMPC) framework with a multi-objective cost function driven by dynamic weights is proposed to improve the energy performance of fuel cell hybrid electric vehicles whilst prolonging their component lifetime. By the use of dynamic weights, the cost function is effectively formulated as the combination of fuel consumption, rate of change of fuel cell power, battery power, the fuel cell efficiency, state of charge of the battery, and their temperatures. In order to enhance the adaptability of the AMPC, a Fuzzy Cognitive Map (FCM) is then newly designed to regulate online the dynamic weights to adjust the importance of each cost component according to the conditions prevailing during driving. A comparative study between the proposed AMPC, a constant weight based NMPC and a conventional NMPC having cost function with fewer objectives has been carried out by means of simulation using a FCHEV model from the simulation tool ADVISOR to illustrate the efficacy of the proposed AMPC.

This paper presents a decision support system that can automatically allocate collision avoidance manoeuvres in the event of a high risk close encounter between two space objects. Decisions are supported by an Intelligent Classification System that combines Dempster-Shafer theory of evidence with Machine Learning to automatically classify conjunctions according to the probability of collision, the uncertainty on the probability of collision, the time to close approach and the cost of a collision avoidance manoeuvre. We propose a simple analytical model that allows for the fast and robust computation of both impulsive and low-thrust manoeuvres under a mix of aleatory and epistemic uncertainty. Aleatory uncertainty is the non-reducible randomness in observation data, dynamic model and parameters, while epistemic uncertainty is the lack of knowledge on system dynamics and observation data, including the model of aleatory uncertainty itself. Dempster-Shafer theory of evidence is used to model the epistemic uncertainty in the calculation of the probability of collision. Some numerical examples are included to show the performance of the collision avoidance manoeuvre optimisation strategy and of the intelligent decision support system.

This paper presents a first-order approximation of ecospheric life cycle impacts from annual global space activities across two scenarios using a streamlined Life Cycle Sustainability Assessment (LCSA). The first scenario considers all space missions launched throughout the 2018 calendar year whilst the second is a futuristic scenario where affordable access to space significantly increases the prevalence of space operations. A new space-specific life cycle database and sustainable design tool called the Strathclyde Space Systems Database (SSSD) has been used to compile the inventory of each scenario and generate results across numerous impact categories. The results for each scenario are then compared against normalised values to portray their contribution towards annual worldwide impacts and their severity in terms of planetary boundaries. This allows the relative life cycle sustainability impacts of space activities to be benchmarked for the first time, forming a basis for evaluation and discussion. Overall, the study highlights that despite the relatively small footprint of the space industry at present, this will likely become much more meaningful in the future based on predicted trends. This places an added importance on addressing potential adverse life cycle impacts within the design process of future space technologies and products.

In an automotive battery pack, many Li-ion cells are connected to meet the energy and power requirement. The micro-resistance spot welding (micro-RSW) process is one of the commonly used joining techniques for the development of cylindrical cell-based battery packs, especially for low to medium volume applications. This paper is focused on identifying the effect of influencing parameters of the micro-RSW process and developing an optimized joining solution to connect a 0.2-mm-thin nickel tab to 18,650 Li-ion battery cells. The effect of welding parameters including weld current, weld time, squeeze time, pre-heat current, pre-heat time, dwell time and hold time were investigated to optimize joint strength. Firstly, the welding pilot runs were conducted between Ni connector and two different thicknesses (i.e. 0.3 mm and 0.4 mm) of Hilumin coupons, representative of negative and positive terminals of 18,650 cylindrical cells. Secondly, it was observed that the weld current had the most significant effect on the weld strength followed by weld time. Finally, at the optimum parameter combination, the live cell welding was conducted between the Ni tab and both positive and negative terminals of LG HG2 18,650 Li-ion cells, and the joints were relatively strong; no intermetallic compounds (IMCs) appeared. Weld microstructure studies provided insightful information on under-weld, good-weld and over-weld characterization and correlated with the joint strength. In addition, electrical contact resistance and temperature rise at the joint is equally important for electric vehicle battery applications. The joint performance was evaluated by analyzing the change in contact resistance and joint temperature rise when different amplitudes of current (i.e. 10 A, 20 A and 30 A) passed through the joints.\ud \ud

This paper presents a systematic literature review (SLR) on cybersecurity in the automotive industry. Using the R tool Bibliometrix, a total of 537 papers related to cybersecurity and the automotive industry were analyzed. First, our paper contributes to academia by showing that research on this topic is grouped into four clusters that correspond to four lines of research: Automotive Security; Vehicle Engineering; Smart Vehicle; IT Security. Second, our paper contributes to the literature by highlighting the existing gaps. On the subject of standards and framework, there are gaps in terms of what the security requirements must be for the vehicle. This is very important since, given the heterogeneity of technology that vehicles from different manufacturers have, the cybersecurity requirements are different. Additionally, a gap can also be observed in the literature on the supply chain, which has a very small number of papers. In general, they do not cover or elaborate on the supply chain security, when, from a manufacturing point of view, it is very important to manage an effective cybersecurity strategy for the vehicles. Moreover, our paper also contributes to managers and policy-makers’ understanding of cybersecurity. We show that adequate implementation of cybersecurity in the automotive must involve a multidimensional perspective. First, it should be a multistage model, ranging from the first stages of design in interconnection with the suppliers to the final stages of use by the customer. Second, it should be a multi-level model, as a consequence of the interconnection of the vehicle’s control systems. Finally, the model should be multi-feedback, structuring the process design as a feedback system, including incident response, diagnosis services and vehicle updates.\ud \ud

Loss models are used to evaluate the aerodynamic performance of axial turbines at the preliminary design stage. The commonly used loss models were derived for air and steam turbines and have not been sufficiently investigated for turbines working with non-conventional working fluids, relevant to new power systems, such as organic fluids and supercritical CO (sCO). Thus, the aim of this study is to explore the deviation between the performance predictions of different loss models, namely Dunham and Came, Kacker and Okapuu, Craig and Cox and Aungier, for non-conventional working fluids where turbines may differ in design and operation than conventional air or steam turbines. Additionally, this paper aims to investigate the effect of the turbine scale on the trends in the performance predictions of these models. Three different case-studies are defined for air, organic Rankine cycle (ORC) and sCO turbines and each one is evaluated at two different scales. It is found that the selected loss models resulted in varying loss predictions; particularly for predicting the losses due to the clearance gap for all small scale designs. Furthermore, large variations were found in predicting the effect of the flow regime on the turbine performance for all models.

The presented work investigates the use of the adjoint state method for the trajectory optimization of a reentry vehicle. The trajectory is optimized using a direct optimal control method with single shooting transcription, where the gradients are evaluated using the adjoint method. This approach leads to a faster optimization process with results comparable to those obtained using the finite differences. The comparison between the adjoint method and the use of the finite differences will be detailed, both in terms of gradient computations and optimisation efficiency.

Autonomous Optical Navigation is essential for the proximity operations of space missions to asteroids that usually have irregular gravity fields. One core component of this navigation strategy is the Image Processing (IP) algorithm that extracts optical observables from images captured by the spacecraft’s onboard camera. Among these observables, the centroid of the asteroid is important to determine the position between the spacecraft and the body, which is the focus of this research. However, the performance of standard IP algorithms is affected and constrained by the features of the images, such as the shape of the asteroid, the illumination conditions and the presence of additional bodies, therefore, the quality of the extracted optical observables is influenced. To address the latter two challenges, this paper develops a Convolutional Neural Networks (CNN)-based IP algorithm and applies it to the Early Characterization Phase (ECP) of the European Space Agency’s HERA mission with the target body of binary asteroid Didymos. This algorithm is capable of estimating the centroid of the primary body successfully with high accuracy and without being affected by the presence of the secondary body or the illumination in the input images. In addition, it can also estimate the centroid of the secondary body when the two bodies are in the same image, which increases the robustness of the overall navigation strategy.

This paper presents a novel approach to the gener-ative design optimisation of a resilient Drone Logistic Network (DLN) for the delivery of medical equipment in Scotland. A DLN is a complex system composed of a high number of different classes of drones and ground infrastructures. The corresponding DLN model is composed of a number of interconnected digital twins of each one of these infrastructures and vehicles, forming a single digital twin of the whole logistic network. The paper proposes a multi-agent bio-inspired optimisation approach based on the analogy with the Physarum Policefalum slime mould that incrementally generates and optimise the DLN. A graph theory methodology is also employed to evaluate the network resilience where random failures, and their cascade effect, are simulated. The different conflicting objectives are aggregated into a single global performance index by using Pascoletti-Serafini scalarisation.

The present survey reviews the definitions of the nonlinear modes and the numerical methods to solve a nonlinear dynamic problem. The progress in application of the damped nonlinear normal modes for structures with frictional damping is reviewed. The related research in the last decade for different friction damping devices are summarized, including the tip shrouded blades, underplatform dampers, blade root damping and friction ring dampers. Furthermore, the experimental works related to nonlinear modal testing are also reviewed. Finally, open problems and future directions are highlighted. We conclude that the nonlinear mode gradually develops from the theoretical stage to engineering stage. Combined with ad-hoc reduced order modeling techniques, onlinear modal analyses have been implemented to reveal the modal characteristics of high-fidelity finite element models of frictionally damped blades/blisks. The Extended Energy Balance Method and Nonlinear Modal Synthesis build a bridge between nonlinear modes and steady-state response, which can significantly improve the efficiency of response-based parameter analysis. Nonlinear modal testing is still in its infancy and cannot be applied to engineering structures yet.

The presence of complex geometries and/or multiple bodies during atmospheric reentry may lead to complex and directional flow features, such as shock waves and shear layers, that need to be correctly predicted to ensure accurate calculation of the aerothermal loads during reentry. Central in ensuring reliable numerical prediction of loads is the adoption of meshes that ensure grid independence and minimize the misalignment between the directional flow features and the grid cell interfaces, a situation that is known to give rise to nonphysical behaviors and spurious oscillations. The use of anisotropic unstructured grid adaptation is here presented as a means to ensure appropriate grid resolution and alignment with directional flow features for cases where the use of structured grids is not always possible or practical. Results highlight the effectiveness and reliability of anisotropic mesh adaptation in successfully predicting the location of shock discontinuities as well as surface aerothermodynamic quantities, while providing results comparable with established approaches relying on structured meshes. Results are presented for single- and multiple-body cases through comparison with experimental data and reference numerical solutions.

IF=5.673; International audience; In this article, we address the simultaneous estimation problem of the lateral speed, the steering input, and the effective engine torque, which play a fundamental role in vehicle handling, stability control, and fault diagnosis of autonomous ground vehicles. Due to the involved longitudinal-lateral coupling dynamics and the presence of unknown inputs (UIs), a new nonlinear observer design technique is proposed to guarantee the asymptotic estimation performance. To this end, we make use of a specific Takagi-Sugeno (TS) fuzzy representation with nonlinear consequents to exactly model the nonlinear vehicle dynamics within a compact set of the vehicle state. This TS fuzzy modeling not only allows reducing significantly the real-time computational effort in estimating the vehicle variables but also enables an effective way to deal with unmeasured nonlinearities. Moreover, via a generalized Luenberger observer structure, the UI decoupling can be achieved without requiring a priori UI information. Using Lyapunov stability arguments, the UI observer design is reformulated as an optimization problem under linear matrix inequalities, which can be effectively solved with standard numerical solvers. The effectiveness of the proposed TS fuzzy UI observer design is demonstrated with real-time hardware-in-the-loop experiments.

The global Connected and Autonomous Mobility industry is growing at a rapid pace. To ensure the successful adoption of connected automated mobility solutions, their safety, reliability and hence the public acceptance are paramount. It is widely known that in order to demonstrate that L3+ automated systems are safer with respect to human drivers, upwards of several millions of miles need to be driven. The only way to efficiently achieve this amount of tests in a timely manner is by using simulations and high fidelity virtual environments. Two key components of being able to test an automated system in a synthetic environment are validated sensor models and noise models for each sensor technology. In fact, the sensors are the element feeding information into the system in order to enable it to safely plan the trajectory and navigate. In this paper, we propose an innovative real-time LiDAR sensor model based on beam propagation and a probabilistic rain model, taking into account raindrop distribution and size. The model can seamlessly run in real-time, synchronised with the visual rendering, in immersive driving simulators, such as the WMG 3xD simulator. The models are developed using Unreal engine, therefore demonstrating that gaming technology can be merged with the Automated Vehicles (AVs) simulation toolchain for the creation and visualization of high fidelity scenarios and for AV accurate testing. This work can be extended to add more sensors and more noise factors or cyberattacks in real-time simulations.

A new engine block with optical access has been designed and manufactured capable of running up to 3000 r/min with the same specification as the unmodified engine. The optical window allowed access to the full length of the liner over a width of 25 mm to investigate the lubricant flow and cavitation at contact point between the rings and cylinder-liner. In addition, it allowed good access into the combustion chamber to allow charged flow, spray and combustion visualisation and measurements using different optical methods. New custom engine management system with build in LabView allowed for the precise full control of the engine. The design of the new optical engine was a great success in producing high quality images of lubricant flow, cavitation formation and development at contact point at different engine speeds ranging from 208 to 3000 r/min and lubricant temperatures (30°C–70°C) using a high-speed camera. The results under motorised operation confirmed that there was no cavitation at contact points during the intake/exhaust strokes due to low in-cylinder presure, while during compression/expansion strokes, with high in-cylinder pressure, considerable cavities were observed, in particular, during the compression stroke. Lubricant temperatures had the effect of promoting cavities both in their intensity and covered ring area up to 50°C as expected. Beyond that, although the cavitation intensity increases further with temperature, its area reduces due to possible collapse of the cavitating bubbles at higher temperature. The change of engine speed from 208 to 800 r/min increased cavitating area considerably by 52% of the ring area and was further increased by 19% at 1000 r/min. After that, the results showed very small increase in cavitation area (1.3% at 2000 r/min) with similar intensity and distribution across the ring.

This paper presents a novel approach to the robust solution of optimal impulsive control problems under aleatory and epistemic uncertainty. The novel approach uses belief Markov decision processes to reformulate the control problem in terms of uncertainty distributions, called beliefs, rather than the realizations of the system states. This formulation leads to the definition of a belief optimal control problem where the cost function and constraints are functions of the uncertainty distributions. The control formulation encompasses orbit determination arcs as well. The belief optimization is solved with a shooting-like transcription and a nonlinear programming solver to optimize the resulting discretized problem. Both aleatory and epistemic uncertainties are propagated with a nonintrusive polynomial expansion to capture the nonlinearities of the dynamics. Finally, this new approach is applied to the robust optimization of a flyby trajectory of the Europa Clipper mission in a scenario characterized by knowledge, execution, and observation uncertainty.

The shock interaction originating from a hypersonic laminar flow over a double-wedge can lead to different flow patterns depending on geometrical parameters such as the lengths and angles of the each wedge, as well as flow parameters such as the mixture and freestream conditions. In this work, the effect of the freestream Mach number on the patterns of shock interaction is numerically investigated for a CO 2-N 2 nonequilibrium flow over different double-wedge geometries. The numerical analysis is performed by solving the laminar Navier–Stokes equations within the framework of a two-temperature model to account for translational–vibrational internal energy transfer. Results show that the size of separated flow regions, both in the compression corner and locations of shock impingement, increase with decreasing Mach number. This revealed to have a significant impact on the patterns of interaction, since the size of a separation region dictates if additional shocks are generated, potentially leading to more complex mechanisms of interaction. It was concluded that decreasing the freestream Mach number has a similar impact as increasing the angle of the second wedge on the pattern of interaction. The parametric study also shows that decreasing the freestream Mach number leads to overall lower surface pressure and heating, as well as smaller regions of thermal equilibrium.

The aging of PV cells reduces their electrical performance i.e., the parasitic parameters are introduced in the solar panel. The shunt resistance (RSh), series resistance (RS), photo current (IPh), diode current (Id), and diffusion constant (a1) are known as parasitic or extraction parameters. Cracks and hotspots reduce the performance of PV cells and result in poor V–I characteristics. Certain tests are carried out over a long period of time to determine the quality of solar cells; for example, 1000 h of testing is comparable to 20 years of operation. The extraction of solar parameters is important for PV modules. The Tabu Search Optimization (TSO) algorithm is a robust meta-heuristic algorithm that was employed in this study for the extraction of parasitic parameters. Particle Swarm Optimization (PSO) and a Genetic lgorithm (GA), as well as other well-known optimization methods, were used to test the proposed method’s correctness. The other approaches included the lightning search algorithm (LSA), gravitational search algorithm (GSA), and pattern search (PS). It can be concluded that the TSO approach extracts all six parameters in a reasonably short period of time. The work presented in this paper was developed and analyzed using a MATLAB-Simulink software environment.

Nonlinear aeroelasticity of a strut-braced ultra-high aspect ratio wing aircraft is investigated using a multi-fidelity simulation environment. A baseline aeroelastic solver based on an unsteady vortex-lattice method and geometrically-exact composite beam model is coupled with a data-driven model order reduction tool based on high-fidelity aerodynamics. This provides sectional corrections, retrieved from a basis created using proper orthogonal decomposition, to the lower-order tools. Those corrections incorporate the three-dimensional and compressibility effects of the full strut-brace wing aircraft geometry and thus result in a computationally-efficient analysis framework suitable for multi-disciplinary design optimization. Verification of the reconstruction process is first presented, and the static and dynamic aeroelasticity of the strut-braced wing configuration are finally investigated.

The re-entry process is distinguished by the presence of several fragments with intricate geometries resulting from the demise process, which may lead to complex features in the re-entry flow. An example of these features is shock impingement, which leads to highly localized loading of pressure and heat flux on the bodies' surface. These loads impact the overall dynamics and cannot be captured using state-of-the-art low-fidelity approaches. A multi-fidelity approach is considered to reduce the uncertainty in predictions during a multiple body re-entry. Such an approach allows the usage of low-fidelity models along with high-fidelity methods such as Computational Fluid Dynamics or Direct Simulation Monte Carlo. This research investigates the formulation and use of a strategy for the automatic selection of the level of fidelity in the computation of aerothermodynamic loads acting on bodies undergoing destructive atmospheric re-entry. Based on the Billig formula, which provides an approximation for the definition of a shock-wave envelope for blunt bodies, a criterion to automatically detect when to transition from low-to-high/high-to-low fidelity is proposed. In the present work, the focus will be on the application of a shock-envelope logic to switch between fidelity methods, exploring the influence of the shock waves from leading fragments onto the following fragments.

Life Cycle Assessment (LCA) is a standardized method of analysis which is used to scientifically quantify environmental impacts associated with products, processes, or services over their entire lifetime. Within the European space sector, its application and perceived importance has exponentially increased in recent years to where it is beginning to become entwined with the procurement process. As such, it is highly probable the technique will become a common element of space mission design within Europe before the end of the decade. In comparison, very little work on space LCA has been conducted within the United States of America (USA), meaning the country is beginning to lag behind others in its ability to account for the life cycle environmental impacts of its space operations. This could become a serious problem for United States (U.S.) original equipment manufacturers (OEMs) and suppliers in the near-term future due to misalignment or non-compliance with European procurement policies, with the potential to cause widespread supply chain disruption. Therefore, this work examines the extent to which the USA has fallen behind Europe on LCA of space assets and the risk this poses. However, the identified benefits of space LCA highlight the method could also act as an indispensable mechanism to further business success within the U.S. space sector beyond solely policy compliance. As such, a set of recommendations are outlined to encourage an intensification of research and development (R&D) on the concept within the U.S. space sector as a foundational principle to a sustainable global space economy.

One of the main challenges for the introduction of Automated Driving Systems (ADSs) is their verification and validation (V&V). Simulation based testing has been widely accepted as an essential aspect of the ADS V&V processes. Simulations are especially useful when exposing the ADS to challenging driving scenarios, as they offer a safe and efficient alternative to real world testing. It is thus suggested that evidence for the safety case for an ADS will include results from both simulation and real-world testing. However, for simulation results to be trusted as part of the safety case of an ADS for its safety assurance, it is essential to prove that the simulation results are representative of the real world, thus validating the simulation platform itself.\ud In this paper, we propose a novel methodology for validating the simulation environments focusing on comparing point cloud data from real lidar sensor and a simulated lidar sensor model. A 3D object dissimilarity metric is proposed to compare between the two maps (real and simulated), to quantify how accurate the simulation is. This metric is tested on collected lidar point cloud data and the simulated point cloud generated in the simulated environment.

While Vortex Generators (VGs) and Gurney Flaps (GFs) are commonly used for airfoil flow control, studies of a combination of the two devices are rare. The present investigation aims at examining the combined effect of VGs and GFs on a 20% thick airfoil. To this end, a wind tunnel investigation coupled with a computational study was performed. The present paper presents force, pressure and Stereo Particle Image Velocimetry measurements, along with Unsteady Reynolds Averaged Navier Stokes simulations.

Reusable launch vehicles (RLV) are slowly emerging as a solution to reduce space access costs, bringing potential benefits from novel breakthrough space application. Whilst space presents an ideal platform for addressing global issues, it raises an "adaptation-mitigation dilemma". Launch vehicles are the only anthropogenic object emitting directly into every layer of the atmosphere, and reusability may introduce additional burdens. Although it may ensure a rational use of materials through the recycling of major components, its potential sustainability gains with respect to equivalent expendable launch vehicles (ELV) has not been quantified. The correct understanding of these are therefore critical to ensure sustainable design choices for space transportation. This study reviews current state of knowledge on launchers environmental impact and eco-design before introducing a preliminary life cycle and atmospheric impact assessment of the different technologies for first stage reusabiltiy. Reusabiltiy showed possible early reductions in material resource depletion which was independent of propellant choice and recovery strategies. In terms of climate forcing, reusability was only beneficial when fully carbon neutral propellant production was assumed for hydrolox, ammolox technologies, and possibly for methalox if soot production is kept under sustainable limits. VTHL performing In-Air-Capturing recoveries also showed reduced climate forcing potential. Stratospheric ozone depletion potential was estimated to increase by 18-34 % for VTVL vehicles, and 12-16% for VTHL with respect to ELV. In addition, high sensitivity with mixture ratios, flight profiles, staging condi- tions and aerodynamic capabilities was identified, which require detailed assessments with higher fidelity design methods. Future launch impacts from large scale space activities were also estimated to no longer be negligible, although some margin for mitigation exists among the various design options, and recent regulatory developments internalizing climate change costs might significantly affect the business case of RLVs. In addition, high altitude atmospheric impacts, particularly those from soot emissions, appear to dominate the potential life cycle impact and uncertainty, especially for hydrocarbon fuelled launch vehicles. This is further exacerbated by the commonly used but unsuitable weighting based on aviation and ground based emissions. These might affect the absolute and relative comparisons significantly and therefore, results from this study must be taken with caution. Future studies should employ state of art atmospheric modelling and adequate approaches to weight the various life cycle impacts, enabling design for mitigation while avoiding burden shifts.

The assembly of 3D printed composites has a wide range of applications for ground preparation of space systems, in-orbit manufacturing, or even in-situ resource utilisation on planetary surfaces. The recent developments in composites additive manufacturing (AM) technologies include indoor experimentation on the International Space Station, and technological demonstrations will follow using satellite platforms on the Low Earth Orbits (LEOs) in the next few years. This review paper surveys AM technologies for varied off-Earth purposes where components or tools made of composite materials become necessary: mechanical, electrical, electrochemical and medical applications. Recommendations are also made on how to utilize AM technologies developed for ground applications, both commercial-off-the-shelf (COTS) and laboratory-based, to reduce development costs and promote sustainability.