The use of optimization procedures for designing acoustic/elastic metamaterials (A/E MMs) has gained significant interest since they enable the efficient attainment of unique functionalities often contradicting. When it comes to vibration attenuation caused by mechanical stress waves, such as impact loads, the dynamic properties of A/E MMs are optimized so that their wave-control ability is maximized. However, the mechanical performance of A/E MMs during the propagation of such waves is normally not evaluated into the design optimization stages. This may compromise not only the load-bearing capacity of MMs, but also their ability in attenuating vibrations. To prevent such effects, we propose a design strategy that incorporates the stress analysis in the early design phase of A/E MMs subjected to an impact load. The effective mass density approach is applied, from which the vibration attenuation is identified at frequency ranges where the resonator moves out-of-phase in relation to the applied excitation. Regarding to the A/E MM mechanical behavior, maximum von Mises stress is calculated through the transient analysis of a unit cell array subjected to a dynamic load. A Pareto front shows a trade-off behavior between the A/E MM functionalities. With that, we emphasize the importance of incorporating the mechanical performance into the design stage of A/E MMs for vibration attenuation of structures undergoing high impact loads, such as installation of foundations by impact hammering. This brings A/E MMs closer to real applications involving energy filtering at specific frequencies from transient loads, designed in an optimized and efficient way.

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The International Maritime Organization aims to achieve full decarbonization by 2050 in response to climate change. This ambitious goal demands well-defined strategies guided by techno-economic assessments. The complexity of global shipping systems makes predicting long-term maritime trade patterns challenging, necessitating scenario-building rather than precise forecasts. Investigating shipping demand scenarios is crucial due to the uncertainty brought by the energy transition and its role as the primary driver of shipping emissions. This paper improves the representation of maritime shipping in Integrated Assessment Models (IAMs) by examining the impacts of climate targets on future shipping demand. A novel econometric model, grounded in advanced gravity theory and integrated with machine-learning algorithms, is proposed to estimate the elasticities of variables in bilateral seaborne trade. By coupling this model with the WITCH IAM, we explore various scenarios, providing deeper insights into trade patterns and their implications. The results show that stricter climate policies and higher carbon taxes reduce GDP due to higher abatement costs, higher fuel prices, and therefore reduced seaborne trade, especially for oil products and containerized cargo. Early adoption of carbon taxes in Europe may shift oil production and consumption patterns, temporarily boosting seaborne trade. Sub-Saharan Africa could experience significant demand growth due to economic and population increases.

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In this study we investigate the mechanochemical regeneration of sodium borohydride (NaBH₄) from a system comprising hydrated sodium metaborate ( [Formula presented] ) and magnesium hydride (MgH₂). We explore the individual and joint impact of key operational parameters (rotational speed, milling time, ball-to-powder ratio (BPR), and molar ratio) on the regeneration yield. Furthermore, a method for quantifying chemical conversion is introduced relying only on water and thus, offering environmental benefits. This approach additionally facilitates the production and storage of a “ready-to-use” NaBH₄ solution with minimal losses at room temperature. Notably, a yield of 90% is achieved, with a 20% reduction in rotational speed compared to prior literature. This research contributes to sustainable hydrogen storage and presents practical advancements in mechanochemical processes.

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Mechanical metamaterials are architected structures with unique functionalities, such as negative Poisson's ratio and negative stiffness, which are widely employed for absorbing energy of quasi-static and impact loads, giving improved mechanical response. Acoustic/elastic metamaterials, their dynamic counterparts, rely on frequency-dependent properties of their microstructure elements, including mass density and bulk modulus, to control the propagation of waves. Although such metamaterials introduced significant contribution for solving independently static and dynamic problems, they were facing certain resistance to their use in real-world engineering problems, mainly because of a lack of integrated systems possessing both mechanical and vibration attenuation performance. Advances in manufacturing processes and material and computational science now enable the creation of hybrid mechanical metamaterials, offering multifunctionality in terms of simultaneous static and dynamic properties, giving them the ability of controlling waves while withstanding the applied loading conditions. Exploring towards this direction, this review paper introduces the hybrid mechanical metamaterials in terms of their design process and multifunctional properties. We emphasize the still remaining challenges and how they can be potentially implemented as engineering solutions.

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Monopiles are the dominant foundation type for offshore wind turbines, accounting for approximately 80% of the installed capacity. Installing offshore monopile foundations on seabeds susceptible to scour erosion requires monopiles to penetrate several pre-installed scour protection rock layers before securing them into the seabed. The accurate prediction of the pile penetration resistance is crucial to ensure successful monopile installations. To complement, and potentially reduce the dependence on the costly and labour-intensive experimental small-scale penetration tests, a numerical model has been developed using the Discrete Element Method (DEM) that captures the discrete nature of interactions between rocks and piles and predicts the resistance during the penetration process. The developed DEM model includes armour and filter rocks represented by multispheres and sand particles represented by spheres. A multistage calibration, verification and validation DEM modelling framework is proposed and examined with small-scale penetration tests conducted using plates and piles in a double-layer scour protection configuration. The sand material model is calibrated and verified using penetrometer tests and the rock material models are calibrated and verified using a plate penetration test. The DEM model with three verified materials predicts the penetration resistance well in small-scale pile penetration tests and proves the validity of the proposed framework. The DEM model presented in this paper facilitates the modelling in areas where traditional continuum-based numerical methods give less accurate predictions and provide insights that are difficult or nearly impossible to obtain through experimental methods.

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Although DEM (Discrete Element Method) was introduced >40 years ago, there are still various challenges in applying it with a certain level of confidence. To develop a realistic material model, calibration, validation, particle shape representation and scaling are well-known challenges that have been studied by various researchers over the past two decades. In addition, once a realistic DEM material model is developed and published in line with open science principles, it is unknown to what extent the calibrated values can be used across software packages. Although a benchmark between 8 open source codes was recently published, it did not cover cohesive materials, rolling friction models, realistic calibrated material behaviour, and commercial software that is widely used in industries.

The aim of this study is to investigate the portability of input parameters between different codes and to identify if software independent calibration is possible. We propose a framework to assist DEM users in industry and academia to reach a software independent DEM simulation when required.

To compare the results between software packages, the framework considers the following aspects: 1) identical implementation of contact models, 2) single contact modelling, 3) bulk level simulation, and 4) identical post-processing. This framework is demonstrated for two contact models (Hertz-Mindlin and Edinburgh Elasto Plastic Adhesive) with rolling friction model and two commercial software packages (EDEM and PFC) but is applicable for any other (open-source) software. Moreover, two realistic calibrated material models are included to cover a range of material characteristics, from free-flowing incompressible materials to cohesive compressible materials. This study shows that individual particle level simulations give identical results, bulk level simulations show differences for both contact models. In general, users should use parameter values from other software packages with caution, especially where critical or sensitive applications are modelled. This paper also highlights the use of novel computation techniques, such as the GPU engine, to achieve practical computation times when modelling industrial applications.@en

The maritime sector accounts for approximately 3% of the global emissions, and to limit these emissions, a transition towards alternative fuels is ongoing. In this work, a novel circular bunkering process for marine vessels is considered, such that a renewable fuel economy for the maritime industry can be realised. The proposed bunkering process is shown in Figure 1, and uses sodium borohydride (NaBH4) as the fuel, due to its favourable gravimetric and volumetric energy density compared to other alternatives [1]. NaBH4 is fed into a reactor during vessel operation, where it reacts with water to form hydrogen and sodium metaborate (NaBO2). While the hydrogen can be used in e.g. fuel cells to power the ship, the NaBO2, also referred to as spent fuel, has to be stored for the remainder of the voyage. Both NaBH4 and NaBO2 are bulk solids with a particle size distribution ranging from a hundred micrometres to several millimetres.
To this moment, NaBH4 has predominantly been used in the chemical industry as a reducing agent [2], and consequently, the mechanical characteristics and the effects of operational conditions such as humidity, temperature, and stress on the behaviour of the material are virtually unknown. However, this knowledge is essential to be able to design the required storage and handling equipment to realise the aforementioned circular bunkering process. Therefore, this work focuses on acquiring the relevant data using experiments. While most experiments show that NaBH4 is initially free-flowing, particular combinations of operational conditions show a significant change in the materials flow characteristics, some results showing very cohesive material or even non-flowing characteristics. Using the acquired experimental data, the Discrete Element Method (DEM) will be used to calibrate, verify, and validate material models, such that the flow characteristics of this novel fuel can be captured numerically. These models can then be used to conceptualise the bunkering process in a virtual environment. Finally, an outlook on how to use the gained insights to develop and design the storage and handling equipment for the proposed circular bunkering process is presented.

References
[1]: M.C. van Benten, J.T. Padding, and D.L. Schott. “Towards Hydrogen-Fuelled Marine Vessels using Solid Hydrogen Carriers”. In: The 14th International Conference on Bulk Materials Storage, Handling and Transportation. Wollongong, Australia, July 2023
[2]: Kaiqiang Zhang et al. “Recent Advances in the Nanocatalyst-Assisted NaBH 4 Reduction of Nitroaromatics in Water”. DOI: 10.1021/acsomega.8b03051
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Segregation of granular materials is a critical challenge in many industries, often aimed at being controlled or minimised. The discrete element method (DEM) offers valuable insights into this phenomenon. However, calibrating DEM models is a crucial, albeit time-consuming, step. Recently, using machine learning (ML)-based surrogate models (SMs) in the calibration process has emerged as a promising solution. Nevertheless, developing such SMs is challenging due to the high number of DEM simulations required for training. Additionally, choosing a suitable ML model is not trivial. This study aims to develop SMs that effectively link particle-particle and particle-wall DEM interaction parameters to segregation of a multi-component mixture. We evaluate several ML models, ranging from artificial neural networks to ensemble learning, that are trained on a very cost-effective dataset, employing Bayesian optimisation with cross-validation to tune their hyperparameters. Next, we introduce a novel transfer learning (TL)-based approach that leverages knowledge from a few scenarios to handle new “unseen” ones. This method enables the construction of adaptive SMs for unseen scenarios, such as a new initial configuration (IC) of granular mixtures, without the need for a full-sized dataset. Our findings indicate that Gaussian process regression (GPR) efficiently builds accurate SMs on a very small dataset. We also demonstrate that only a few samples are required to build an accurate SM for the unseen IC, which significantly reduces the data preparation burden. By incorporating one and five samples from unseen scenarios to update the TL-GPR-based surrogate model, the SM’s performance (based on ) on unseen scenarios improves by 17 and 47%, respectively. The insights and methodology presented in this study will facilitate and accelerate the development of accurate SMs for DEM calibration, assisting in developing reliable DEM models in a shorter timeframe.@en

Bed permeability is a crucial factor in blast furnace performance which depends on the material distribution achieved through charging. Since a homogeneous bed of pellet and sinter is recommended, it is crucial to understand whether segregation of the pellet-sinter mixture occurs during charging. The Discrete Element Method is useful in this regard; however, simulations of pellet-sinter mixture charging currently lack credibility since pellet-sinter interaction parameters have not yet been calibrated and validated. Determining pellet-sinter interaction parameters will require significant efforts, so it is useful to know whether mixture segregation and the resulting bed permeability are sensitive to these parameters. In this work, we investigate to what extent the restitution coefficient, sliding friction coefficient and rolling friction coefficient between pellet and sinter affect segregation during bed formation and the resulting permeability in terms of porosity using a simplified charging setup. The investigation is done for different mixture compositions and flow velocities, and analysis settings including sample size and sampling directions. We conclude that all parameters affect segregation and porosity, regardless of the composition and velocity. Hence, all mixture parameters including the interaction parameters between the components must be carefully calibrated when developing a model for predicting permeability.

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Correction to: Scientific Reportshttps://doi.org/10.1038/s41598-024-78455-7, published online 06 November 2024 The original version of this Article contained typographical errors in Equations. In Equation 8, where now reads: In Equation 17, where now reads: In Equation 18, where now reads: In Equation 24, where now reads: The original Article has been corrected.

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Soft robotics has significant interest within the industrial applications due to its advantages in flexibility and adaptability. Nevertheless, its potential is challenged by low stiffness and limited deformability, particularly in large-scale application scenarios such as underwater and offshore engineering. The integration of smart materials and morphing structures presents a promising avenue for enhancing the capabilities of soft robotic systems, especially in large deformation and variations in stiffness. In this study, we propose a multiple smart materials based mechanically intelligent structure devised through a model-based design framework. Specifically, the intelligent structure incorporates smart hydrogel and shape memory polymer (SMP). Employing the finite element method (FEM), we simulated the complex interactions among smart material to analyze the performance characteristics of the intelligent structure. The results demonstrate that, utilizing smart hydrogel and shape memory polymer (SMP) can effectively attain large deformation and exhibit variable stiffness due to the shape memory effect. Besides, the shape-morphing structures exhibit customized behaviours including bending, curling, and elongation, all while reducing reliance on external power sources. In conclusion, utilizing multiple smart materials within the model-based design framework offers an efficient approach for developing mechanically intelligent structure capable of complex deformations and variable stiffness, thereby providing support for underwater or offshore engineering applications.@en

In this chapter, the work that is being done in relation to machine-cargo interactions relevant to deep-sea mining is elaborated. To ensure safe and efficient operations across the entire mining value chain, it is important to be aware of the implications of certain design and process decisions. An overview of mechanisms influencing the mechanical response of the bulk materials and the main effects leading to (mechanical) degradation of the ore, e.g., fragmentation, abrasion, is presented. Although the concepts are applicable to each of the deep-sea deposits, our focus is on polymetallic nodules in a riser-based concept. Results are discussed of experiments in which nodules are fragmented due to particle-particle collisions and collisions with different handling equipment, such as the seabed harvester, riser pipe, pump impeller, pipe bends, etc. Next to fragmentation, degradation due to abrasion occurs due to particles rolling, sliding, and colliding with each other, resulting in the generation of nodule fines. Based on the same set of nodules, mechanical bulk properties of dry and wet nodules are studied. The obtained results provide relevant insights for the design of the nodules handling equipment. Furthermore, modelling approaches applied to other fragile bulk materials where the breakage and generation of fines play an important role are outlined. It is described how these modelling approaches can assist in the design of handling equipment, and recommendations are given for next steps to further optimize their design.@en

Soft grippers show adaptability and flexibility in grasping irregularly shaped and fragile objects. However, the low loading capacity and less deformation limit the soft gripper for developing large-scale applications. To overcome these limitations, we propose a new concept of a soft actuator with engineered smart particles. The proposed soft actuator is a dual-chamber programmable structure made from an elastic membrane filled with different particles, which can be driven by expanding particle volume or flexible membrane shrinking. Compared to traditional pneumatic or particle-jamming actuators, we use a combination of granular materials and smart materials, which delivers better active performances of large-scale deformation and variable stiffness. The coupled numerical model of the discrete element method and the finite element method is used to demonstrate the concept. The results indicated that the proposed soft gripper achieves the functionality of large deformation by a shrinking membrane or expanding particles. By controlling different design parameters, the actuator bends up to 138 deg, and the stiffness is up to a maximum of nine times of the pneumatic actuator. Additionally, the bending angle and deflections of the gripper actuator first increase and then drop down with increasing particle diameter ratio, actuator length, and elastic modulus of membrane material. Hence, the choice of different parameters must be in a specific range to achieve the required deformation. In conclusion, the soft-grasping gripper actuator can realize large bending deformation and shows potential for developing soft grippers in multi-scale physical scenarios.

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Segregation of granular materials is a critical phenomenon in various industries, such as food processing, pharmaceuticals, and mining. The Discrete Element Method (DEM) is an effective tool for gaining insight into granular segregation by providing particle-level information and the freedom to model mixtures that are often difficult or impossible to achieve through experiments. To ensure realistic material behaviour and correct representation of segregation, it is essential to calibrate the model parameters systematically. However, in the context of multi-component segregation, it is extremely challenging and computationally expensive to consider all parameters in the calibration procedure since interaction parameters between components must also be considered. This work aims to identify the dominant DEM parameters for modelling multi-component segregation during hopper discharge, chute flow and heap formation in a mixture of pellet and sinter. Utilising a representative example of a multi-component mixture with different sizes, densities and shapes used in blast furnaces, the investigation is done for various initial configurations as well as various mass ratios of the mixture. Our findings revealed that, while only particle-geometry interaction parameters dominate the segregation after the hopper and chute flow, particle-particle parameters are also significant for segregation in the heap. We also demonstrated that the downstream segregation is significantly influenced by the segregation upstream. Moreover, we found that the effect of pellet-sinter interactions is negligible. This research provides insights into the dominant DEM parameters, facilitating more efficient and robust calibration of multi-component models in future research endeavours.

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Underwater noise resulting from the monopile driving process can cause severe damage to marine wildlife, such as hearing injury, behavioral disturbance, or even death. Although current noise-attenuation techniques used in this process have shown a significant noise reduction at high frequency ranges, mitigating low-frequency noise is still extremely challenging. To address the problem, here we propose an elastic metamaterial-based structure composed of single-phase resonant structures. The proposed structure, which we call a meta-interface, is introduced between the monopile and the hammer and is used to remove energy from the input signal associated with high noise levels. To that end, we first identify the frequency ranges associated with high sound pressure levels, which were shown to be related to the monopile's eigenmodes. Then we design the meta-interface's periodic unit cells so that the elastic/acoustic waves at identified frequency ranges are attenuated. A meta-interface is then realized by replicating the unit cell along the monopile wall (matching the thickness) to form a ring-shaped layer, and then by stacking up these concentric layers. A frequency analysis of the pile driving system with the meta-interface shows that the new noise levels attain a significant attenuation in frequency ranges lower than 1000Hz. This demonstrates a novel solution for the low-frequency underwater noise issue during the hammering of offshore monopiles.

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A convex pattern surface is proposed and optimized to mitigate the sliding wear of bulk handling equipment caused by interaction with bulk solids. This work investigates the effectiveness of the convex pattern surface on wear reduction during interactions with non-spherical particles. Multiple representative particles, obtained through a sampling method, are reconstructed using a photogrammetry technique. Two contact parameters between particles are calibrated through shear box and drawdown tests to ensure flow behavior similar to the real material. The numerical results indicate that the convex pattern surface can effectively reduce wear compared to a plain sample when involving both spherical and non-spherical particles. For a plain sample, the wear volume remains independent of particle shapes and increases linearly with numerical revolutions. For the convex pattern surface, the wear volume demonstrates a quadratic relationship with the test revolutions as the deformation of convex elements weakens the effectiveness of the sample on wear reduction. The particle flow behavior analysis reveals that the convex pattern surface experiences the lowest wear volume when in contact with non-spherical particles. This can be attributed to the non-spherical particles sliding shorter distances and rotating with higher angular velocities on the convex pattern surface.

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As climate change continues to pose a significant threat to our planet, international maritime shipping plays a crucial role in mitigation efforts. Recognizing the urgency, the International Maritime Organization (IMO) has revised its targets, now aiming for full decarbonization by 2050. However, there is no established pathway to get to the target. To achieve this, there is a need for models depicting possible futures of the maritime sector, and finding feasible pathways. This research aims to find the most suitable way to develop models to find pathways toward decarbonization targets. This involves evaluating existing ranges and scenarios to understand current estimations and their underlying assumptions and assessing the most suitable modeling methods based on defined criteria. Considering the context, the most suitable models for this objective should perform on a global scale. They should include dynamics between shipping demand & supply as well as the derived fuel demand and supply and emissions; integrate the sector with other parts of the economy; incorporate various technologies into the framework; and span multiple scenarios. The study has two main parts. First, existing scenarios on the future of maritime shipping are analyzed to identify current estimations and assumptions impacting these estimations. Second, various modeling frameworks are assessed against the defined criteria to identify the most suitable modeling structure for achieving the decarbonization targets. Many projections do not meet the IMO’s updated targets, highlighting the need for a paradigm shift in setting targets and finding feasible pathways rather than focusing solely on individual measures. Integrated Assessment Models (IAMs) have been identified as suitable for such projections and policy analysis, although international shipping is often underrepresented in current models. Future research should combine the insights of sectoral models in integrated frameworks such as IAMs to develop integrated strategies to investigate pathways to achieve zero-emission targets. The ultimate goal is to understand how to effectively reduce the sector’s emissions and achieve more environmentally friendly international maritime shipping.

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Greenhouse gas emissions drive global warming, posing significant risks to ecosystems and human society. The maritime sector contributes approximately 3% of global emissions, leading the International Maritime Organization (IMO) to target a 40% reduction in emissions by 2030 and 70% by 2050, compared to 2008 levels. This reduction can be achieved by improving vessel efficiency or adopting alternative fuels such as hydrogen. Traditionally, hydrogen is stored as a gas, liquid, or cryocompressed, but these methods have drawbacks, including high pressure and energy demands. A promising alternative is sodium borohydride (NaBH4), a solid hydrogen carrier that offers high volumetric energy density and safe storage at ambient conditions. When used in maritime vessels, NaBH4 reacts with water to produce hydrogen and a byproduct, which is stored and later regenerated onshore. Both NaBH4 and its spent fuel are granular materials, requiring specific handling equipment. This paper aims to identify the mechanical characteristics of NaBH4 and its spent fuel to design appropriate storage and handling systems using the Discrete Element Method (DEM).@en

Clay is a notoriously challenging material to dredge. Due to its adhesion and plastic behaviour, it may clog the suction head and clay balls could form down the pipe line. This will raise difficulties in estimating the production or the required power and increase the risk of downtime. As this is an expensive risk for the dredging industry, there is a lot of literature on the cutting of clay in dredging. However it is focused on the forces and stress distribution near the blade tip. Unfortunately, there is little information on the influence of adhesion and plasticity of clay on the deformation and the sliding of the chip over the tool and their contribution to the total cutting forces. Current models are likely to lack some key details of clay behaviour.
In this review, published results from experiments of cutting in clay have been aggregated. An attempt has been made to evaluate the results uniformly with dimensionless parameters derived by the Buckingham-pi method. The state of the art of models for cutting highly plastic materials is presented, providing a more detailed description of the excavation processes in submerged clay. The test results have been compared with the those existing models. This provides insights regarding chip formation and the deformation of the chip as it moves along the tool. This knowledge provides a basis for solutions needed to avoid clogging of equipment and the occurrence of clay balls.
This review is part of the CHiPS project, which investigates rapid large plastic deformations in submerged clay for Cutting Highly Plastic Soils.
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Soft robots developed by smart materials and structures present advantages in adaptability and flexibility for tailored functions in complex environment. However, conventional design methodologies, which heavily depend on experimental procedures, present obstacles to rapid and efficient design iterations. Thus, employing model-based design emerges as an effective approach to support the designs of soft actuators. In this study, the building blocks was proposed employed by model-based design strategy to investigate the novel actuation approach in mechanically intelligent morphing structures. The simulation results demonstrate, utilizing model-based strategy is the efficient way to develop building blocks of different morphing structures. Furthermore, the combined effort of various smart materials enables varied adapabilities and flexibilities. In summary, by integrating different modeling approaches, material models, and contact models, it is feasible to efficiently design the inteligient structures based on the tailored building blocks to specific requirements, thereby providing guidance and support for engineering design.@en