In blast furnace ironmaking, a mixture of iron ore pellets and sinter is charged in layers at the furnace top, with particle velocities reaching up to ∼10 m/s at the stock surface. The inherent differences in particle size, shape, and density between pellets and sinter pose challenges for maintaining a uniform mixture during this high-velocity charging, leading to segregation and uneven material distribution. This non-uniformity can negatively affect furnace efficiency and stability. Understanding segregation during charging is therefore crucial for optimizing the ironmaking process. The Discrete Element Method (DEM) can offer valuable insights, provided that the model parameters are calibrated and verified. This study presents a calibrated DEM model for a pellet–sinter mixture with a 50–50 mass ratio of both components. A novel high-velocity laboratory setup was used to simultaneously measure five different key performance indicators (KPIs) related to flow and packing behavior at various discharge heights, corresponding to different flow velocities. Calibration was performed at the highest flow velocity, representative of actual blast furnace conditions. The process involved creating response surface models for each KPI and using a multi-objective optimization approach with a desirability function to determine the model parameters. A step-wise calibration strategy was employed, first optimizing pellet and sinter interaction parameters individually, followed by calibration of the pellet–sinter interaction parameters. This approach proved effective, as the calibrated model accurately reproduced experimental data. Results also suggest that the calibration outcome is flow-invariant in this setup, with the model successfully predicting flow and packing behavior at lower discharge heights.

Powder flowability underlies reliable solids handling, influencing dosing accuracy and production stability. Wood powders are usually cohesive and susceptible to flow problems like bridging because of their irregular, fibrous particles that are hygroscopic and heterogeneous. Two lignocellulosic powders were tested: spruce (softwood) and poplar (hardwood). Their particle size distribution, particle shape, and density were measured experimentally. Crucially, the interparticle parameters that govern powder bulk behavior, which are the cohesion energy density (CED), rolling friction coefficient (μᵣ), and sliding friction coefficient (μₛ), are not directly measurable at the scale and morphological complexity of fibrous wood particles. Therefore, using the Discrete Element Method (DEM), (μₛ,μᵣ, CED) were identified as effective DEM parameters by inverse calibration against rotating drum tests. A novel calibration workflow was developed to compare DEM simulations with real rotating drum experiment indicators, which can be used for unconfined, dynamic flow. These indicators correspond to newly discovered macroscopic flow descriptors that are processed from the powder bed: average projected area Area¯, its fluctuation σArea, and the average surface profile irregularity r²¯. Wood particles were modeled as multi-sphere clumps with different sizes to balance realism and computational cost. The calibrated parameters were: spruce—μ_s=0.10, μ_r=0.367, CED=130 kJ/m³; poplar—μ_s=0.10, μ_r=0.772, CED=100 kJ/m³. Following a comprehensive results analysis, increasing CED and friction parameters deteriorates powder unconfined flowability by promoting agglomeration and particle interlocking. The resulting calibrated DEM inputs provide a baseline for predicting and improving the handling of wood powders in hoppers, feeders, and conveying screws.

This study introduces a computational framework for modelling raw chicken breast fillets using the Discrete Element Method (DEM), aimed at providing a baseline efficient simulation model for large-scale poultry handling processes. A bonded multi-sphere meta-particle representation was developed and calibrated through mechanical testing of raw fillets. Compression experiments yielded a Young’s modulus of approximately 48.6 kPa, which informed the stiffness properties of the DEM sub-particle assembly. Numerical Design of Experiments (DoEs) highlighted the need for an unbalanced ratio between normal and shear bond stiffness to ensure correct damping behaviour and preserve realistic flexibility. The framework was validated using a full-scale hopper–conveyor discharge experiment, demonstrating the model’s ability to reproduce key physical behaviours such as large deformations, curling during discharge, and the transition between jammed and free-flow regimes. The simulation closely matched the measured discharge rate, with all chicken fillets discharged within 4 s at a 6 cm gate opening height. The proposed model required approximately 9 mins to simulate a 10-second industrial-scale process, underscoring the model’s practical suitability for simulation-aided design and optimisation of poultry processing equipment.

While the Discrete Element Method (DEM) provides high-fidelity insights into granular processes like high-energy ball milling, its computational cost can become prohibitive when exploring extensive parameter spaces required for scale-up. This limitation hinders the rapid design and optimization cycles crucial for emerging applications, like mechanochemistry. Surrogate modeling offers a promising path to overcome these computational barriers, yet existing approaches often struggle to accurately represent the complex, moving boundary conditions typical of milling equipment. In this work, we leverage a Signed Distance Function Graph Neural Network (SGN) surrogate tailored to the high-energy, moving-boundary regime of the Emax mill. Trained on DEM data, the SGN jointly predicts particle kinematics for recursive roll-out and a mechanochemistry-relevant global quantity, the global dissipated energy. The model exhibits strong generalization to unseen motion trajectories and moderate jar-shape edits without retraining, while operating with a timestep over 100x larger than required by DEM. In a CPU-only comparison, it achieves a minimum of 6.6× wall-clock speedup. This approach provides a powerful and promising technique for the simulation, analysis, and design optimization of high-energy ball milling equipment.

Soft robotics requires structural systems capable of performing complex and programmable deformations to adapt to unstructured or dynamic environments. Shape memory materials (SMMs) offer a promising solution owing to their shape memory effect and stimulus-responsive adaptability. However, actuators relying on a single type of SMM are often constrained by nonlinear actuation behavior and limited stiffness variation, which restrict their ability to achieve coordinated, multifunctional responses. Addressing these challenges, this study introduces a hybrid programmable morphing structure that integrates a shape memory polymer (SMP) and a shape memory alloy (SMA) to realize cooperative actuation and adaptive stiffness variation within a single unit. In the proposed configuration, the SMA springs act as thermally activated actuators that generate deformation. The SMP cylindrical core employs its shape memory effect to realize reversible shape locking and serves as a thermal switch that enables controlled stiffness variation through temperature regulation. A coupled numerical model was established to describe the cooperative behavior between the SMA and SMP components, and the numerical results were validated through experimental testing. The agreement between simulations and experiments confirms the feasibility and repeatability of the proposed design. The structure achieves a maximum bending angle of 55° under dual-SMA actuation and 42° under single-SMA actuation, while maintaining any intermediate shape during thermal cycling. Furthermore, the hybrid system demonstrates a reversible six-fold increase in stiffness and a motion range extending up to three times its original length, representing a significant improvement over conventional single-material soft actuator. Moreover, the proposed hybrid structure offers a flexible strategy for programmable morphing and demonstrates scalable applicability in practical applications, such as adaptive grasping, reconfigurable locomotion, and environmental exploration. In conclusion, this work provides a feasible and generalizable framework for integrating multiple SMM into programmable morphing structures which can be applied into multifunctional soft robotic systems.

Segregation of the ferrous burden during blast furnace (BF) charging can cause uneven layer formation at the furnace throat, reducing bed permeability and disrupting gas–solid interaction. This study applies a discrete element method (DEM) model to the industrial-scale BF charging system (from the skip car to top hopper discharge) to examine segregation under real operating conditions. The model includes the full ferrous mixture (pellets, sinter, lump ore, and nut coke) and the real-scale geometries. A reference case representing current practice is analysed in detail and compared with systematically varied case studies. The results show that segregation generally decreases from the skip car to the top hopper due to partial remixing, but strong segregation is still observed. Lump ore and nut coke exhibit the strongest segregation, while pellets remain the least segregated. The order of pellets and sinter in the weighing bunkers strongly influences their segregation patterns, whereas variations in the sinter particle size distribution (PSD) and particle shape have only limited effects. The insights from this study provide a basis for developing practical strategies to mitigate segregation in industrial BF charging.

Micro-scale mechanisms, such as inter-particle and particle-fluid interactions, govern the behaviour of granular systems. While particle-scale simulations provide detailed insights into these interactions, their computational cost is often prohibitive. At a recent Lorentz Center Workshop on “Machine Learning for Discrete Granular Media”, researchers explored how machine learning approaches can aid the development of constitutive laws and efficient data-driven surrogates for granular materials while also addressing uncertainty quantification. Attended by researchers from both the granular materials (GM) and machine learning (ML) communities, the workshop brought the ML community up to date with GM challenges. This position paper emerged from the workshop discussions. In this position paper, we define granular materials and identify seven key challenges that characterise their distinctive behaviour across various scales and regimes–ranging from gas-like to fluid-like and solid-like. Addressing these challenges is essential for developing robust and efficient models for the digital twinning of granular systems in various industrial applications. To showcase the potential of ML to the GM community, we present classical and emerging machine/deep learning techniques that have been, or could be, applied to granular materials. We reviewed sequence-based learning models for path-dependent constitutive behaviour, followed by encoder-decoder type models for representing high-dimensional data in reduced spaces. We then explore graph neural networks and recent advances in neural operator learning. The latter captures the emerging field evolution of interacting particles via efficient latent space representation. Lastly, we discuss model-order reduction and probabilistic learning techniques for high-dimensional parameterised systems, both of which are crucial for quantifying and incorporating uncertainties arising from physics-based and data-driven models. We present a typical workflow aimed at unifying data structures and modelling pipelines and guiding readers through the selection, training, and deployment of ML surrogates for granular material simulations. Finally, we illustrate the workflow’s practical use with two representative examples, focusing on granular materials in solid-like and fluid-like regimes.

Calibration of discrete element method (DEM) models is crucial for the realistic simulation of granular materials. However, it remains a challenging task, especially for multi-component mixtures due to their higher complexity and larger number of parameters involved. This study presents a systematic and computationally efficient calibration framework designed to address these challenges, focusing on pellet-sinter mixtures, as a representative case of two-component mixtures commonly used in blast furnace steelmaking. The framework integrates sensitivity analysis, machine learning-based surrogate modelling with adaptive sampling, and genetic algorithm-driven optimisation techniques to minimise the number of required DEM simulations. Using this approach, we achieved a high-accuracy surrogate model (R² = 0.95) for seven DEM parameters with only 110 data points, highlighting the efficiency and robustness of the framework. These parameters were successfully calibrated with a relative error of less than 2 %. Moreover, the calibrated parameters for the base case (i.e., 50–50 pellet-sinter mass ratio) remained valid across different mass ratios and layering orders, eliminating the need for recalibration. Overall, the proposed framework offers a reliable, cost-effective, and adaptable solution for DEM calibration of two-component mixtures. Its flexibility and efficiency make it a promising tool for extending to more complex systems, facilitating the development of DEM models for a wide range of industrial applications involving granular mixtures.

Locomotion over granular terrain poses significant challenges for autonomous robotic systems, particularly in coastal regions characterized by loose, shifting sands. To optimize the locomotion on these challenging terrains, a simulation-aided design approach was used to develop a soft, shape-adapting, wheeled locomotion system. A co-simulation framework combining the discrete element method (DEM) and multibody dynamics (MBD) is employed to simulate the locomotion of a wheeled robot on varying sandy soils, covering both dry and wet sandy soil conditions. A shape-adapting wheel design is proposed, incorporating soft, inflatable elements that enable the wheel to transform between lugged and circular configurations. A discretized flexbody approach is adopted to model the interactions between the sandy soil and the soft, flexible bodies of the shape-adapting wheel design. Simulation results demonstrate improved performance of the shape-adapting wheels across a variety of sandy terrains, including slopes and obstacles. Integrating softness into the wheel improves obstacle climbing performance, while a lugged wheel configuration performs particularly well on loose, dry sandy slopes. This DEM-MBD co-simulation further enables efficient evaluation of locomotion strategies without the need for extensive physical prototyping.

To reduce global emissions, hydrogen is increasingly considered as an energy carrier for renewable energy storage. However, traditional storage methods for hydrogen such as compression or liquefaction require high pressures, extremely low temperatures, and still result in a low volumetric energy density. As a solution, sodium borohydride (NaBH₄) is proposed as an alternative method to store hydrogen. NaBH₄ is a granular material that can be stored using ambient temperature and pressure, and has a relatively high volumetric and gravimetric energy density compared to traditional hydrogen storage. This paper explores the application of NaBH₄ as a fuel in the maritime industry, and elaborates on how the use of NaBH₄ leads to a circular bunkering (refuelling) process. By using hydrolysis to extract hydrogen from NaBH₄ during vessel operation, a so called spent fuel remains and needs to be stored on the vessel until next port call. Additionally, examples of various bunkering equipment that can be used to design the circular bunkering process of NaBH₄ are presented. Moreover, it explains how design of bunkering equipment depends on the mechanical characteristics of the fuel and spent fuel. The main finding of this work is that NaBH₄ is a promising solution for a sustainable future. Before NaBH₄ can be used as a fuel, vessels and ports need to be adapted to facilitate circular bunkering with such a novel solid-state energy carrier.

Conventional scour protection installation around monopiles for offshore wind farms involves placement of smaller filter layer rocks and larger armor layer rocks in two separate operations, requiring multiple visits of the rock dumping vessel to the site which increases costs, time spent offshore by the vessels, and consequently carbon emissions. The joint industry project Optimizing Pile Installation through Scour Protection (OPIS), established in 2023, helps reaching the target of energy transmission by streamlining the pile and scour protection installations, reducing costs and carbon emissions associated with offshore wind developments. The project investigates the technical feasibility of pile installation through coarse rock to enhance the feasibility of the operation. A series of small and medium scale laboratory experiments has been conducted, considering different scour protection designs such as single- and double-layer systems, and different rock densities (high and normal density rocks). This paper delves into the physical modelling aspects of the OPIS research project. Furthermore, this contribution elaborates on the design and intricacies of the scaled laboratory tests, providing in-depth insights into the design and implementation of laboratory setups, along with a detailed account of experimental procedures. Moreover, preliminary results and challenges encountered during the experiments are discussed, and innovative solutions devised to overcome specific challenges are highlighted.

Clay is a notoriously challenging material to dredge. Due to its adhesion and plastic behaviour, it may clog the suction head and/or clay balls could form in the pipeline. This will raise difficulties in estimating the production, the required power and increase the risk of downtime. As this is an expensive risk for the dredging industry, the cutting process requires in depth research to achieve better understanding of the process, to prevent problems and to mitigate risks. Available literature on clay deformation and soil cutting has been reviewed. Important topics for the cutting process are the interaction between the clay sliding over the blade and the resulting macro deformation of the chip. Various cutting regimes can be distinguished, including the: Flow regime, Tear regime, Curling regime, etc. Additionally the best practices for soil bin experiments have been included. Review of the available literature and analysis of published models is used to design a soil bin experiment dedicated to test the process under conditions relevant for the dredging industry. The objective of the CHiPS project is to study cutting regime transitions for dimensionless parameter groups of soil properties and operating conditions. Transitions range from static traction problems on soft mud to grinding action on stiff clay. Preliminary results and analysis of these clay cutting experiments are presented. The test rig developed for the CHiPS project is functionally performing satisfactorily, but requires a stronger drive to test high-strength soils.

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.

The growth of offshore wind farms is accelerating to meet the renewable energy target by 2030, driving the development of larger offshore wind turbines (OWTs) to boost energy capacity. To support these OWTs, large monopiles are being installed by using impact hammers, which in turn emit low-frequency underwater noise, posing challenges for traditional noise mitigation systems and increasing risks to marine life. To address this, a metamaterial-based cushion (meta-cushion) was proposed, embedding resonators to filter longitudinal waves associated with high underwater noise levels. While prior work has demonstrated the meta-cushion's noise attenuation potential, design guidelines are required for adaptation to various monopile installations. This paper introduces, for the first time, a design methodology for the meta-cushion, which based on the input parameters of the monopile system, it details the procedure for selecting the resonant elements contributing to the attenuation performance and their spatial arrangement on the cushion for enhancing mechanical performance. Such performance indicators are evaluated via finite element simulations and experimental modal analyses. The methodology concludes with a nondimensional study of the spiral resonator, which showed the best attenuation response in experiments, exploring its behavior under varying material and geometric parameters. This methodology enables the development of meta-cushions adaptable to monopile installations under any environmental conditions.

Sodium borohydride (NaBH4) is increasingly considered as an alternative fuel for maritime vessels due to its relatively high energy density. When stored in dry solid form, it is a granular material, similar to coal, starch, and iron ore. As NaBH4 is historically used in the chemical industry in aqueous solutions, virtually no details regarding its behaviour as a solid granular material are known. Therefore, after determining particle properties such as size, shape, and density, this study characterises granular NaBH4 in three flow regimes using three experimental setups. Ring shear tests are used for the quasi-static regime, ledge tests for the dense flow regime, and rotating drum tests characterise both dense and gaseous flow, depending on the rotational speed. Various operational conditions, including temperature, humidity, time consolidation, and handling stresses, are taken into account. Experimental results demonstrate that above a threshold temperature and humidity, NaBH4 readily absorbs moisture from ambient air but remains free-flowing for most scenarios. However, time consolidation can transform this free-flowing material into a very cohesive substance. While this cohesiveness is reversible, requiring minimal agitation, the transformation from free-flowing to cohesive is accelerated by elevated moisture contents and a reduced particle size. Additionally, handling stresses were found to have minimal effect on the flow behaviour and characteristics of NaBH4. These findings are ultimately used to derive implications for the design of handling and storage equipment for NaBH4, enabling its use as an alternative fuel for maritime vessels.

High-energy ball milling is a versatile method utilized in mechanochemical reactions and material transformations. Understanding and characterizing the relevant mechanical variables is crucial for the optimization and up-scaling of these processes. To achieve this, the present study delves into differentiating the contributions of normal and tangential interactions during high-energy collisions. Using Discrete Element Method (DEM) simulations, we characterize how operational parameters influence these energy dissipation modes, emphasizing the significance of tangential interactions. Our analysis also reveals how different operational parameters such as ball size, fill ratio, and rotational speed affect the mechanical action inside the milling jar giving rise to multiple operating zones where different modes of energy dissipation can thrive. Finally, we present master curves that generalize findings across a wide range of configurations, offering a tool for characterizing and predicting mechanochemical processes beyond the presented cases. These results provide a robust framework for improving mechanochemical reaction efficiency, and equipment design.

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.