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The advances of mechatronic system design and system integration have shown improvements in functionality, performance and energy efficiency in many applications across industries, from autonomous ground vehicles and drones to conveyor belts. This trend has been adopted in some industries more than others. The design of equipment to handle granular or bulk material is commonly based on traditional approaches. Therefore, introducing mechatronic concepts in the design procedure can enable new possibilities, such as sensor integration and data analyses, adaptability and control. The efficiency of bulk material handling equipment in ports, agriculture and food processing is heavily influenced by the operational conditions. Typically, a piece of equipment is designed for defined operational conditions when the maximum performance can be achieved. In this work the concept of adaptability to varying operational conditions is explored by understanding the technologies implemented in other industries and the feasibility to be implemented in the bulk handling equipment design. Sensing technology, actuation and adaptability are systematically presented in this work to support the design process of the next generation of bulk handling equipment. This will pave the way for incorporating the technological trends in the design, such as: sustainability, “smartness”, Internet of Things, Industry 4.0, digital twin and machine learning. Adaptive mechatronic solutions will play a crucial role in generating and implementing innovative sustainable solutions for bulk handling equipment., Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Transport Engineering and Logistics

The design of machinery for handling granular materials relies mainly on empirical methods and in-house engineering knowledge. This traditional approach provides incremental improvements that are often limited. Advancements in simulation and optimization can offer a promising alternative approach. Most of the research involved in improving or optimizing equipment design does not include the realistic performance of the new prototype and as such it is uncertain that the predicted performance is also guaranteed in practice. In this study, a design framework for a new generation of machinery handling granular materials, grabs, has been established that includes a full-scale validation step. This has been proven to lead to a breakthrough in equipment design. This design framework uses a co-simulation between Discrete Element Method (DEM) and Multi Body Dynamics (MBD), thus, capturing operational conditions in full-scale. The DEM simulation supported design step integrated as the main step to generate new prototypes. The performance of the prototype is evaluated by conducting full-scale experiments, thus validating the adequacy of the new design as well as the accuracy of the co-simulation. Through this a full design cycle has been fulfilled and a validated model has been achieved that is independent of specific design configurations., Transport Engineering and Logistics

This paper presents a multi-step DEM calibration procedure for cohesive solid materials, incorporating feasibility in finding a non-empty solution space and definiteness in capturing bulk responses independently of calibration targets. Our procedure follows four steps: (I) feasibility; (II) screening of DEM variables; (III) surrogate modeling-based optimization; and (IV) verification. Both types of input parameter, continuous (e.g. coefficient of static friction) and categorical (e.g. contact module), can be used in our calibration procedure. The cohesive and stress-history-dependent behavior of a moist iron ore sample is replicated using experimental data from four different laboratory tests, such as a ring shear test. This results in a high number of bulk responses (i.e. ≥ 4) as calibration targets in combination with a high number of significant DEM input variables (i.e. > 2) in the calibration procedure. Coefficient of static friction, surface energy, and particle shear modulus are found to be the most significant continuous variables for the simulated processes. The optimal DEM parameter set and its definiteness are verified using 20 different bulk response values. The multi-step optimization framework thus can be used to calibrate material models when both a high number of input variables (i.e. > 2) and a high number of calibration targets (i.e. ≥ 4) are involved., Transport Engineering and Logistics, Offshore and Dredging Engineering, Rivers, Ports, Waterways and Dredging Engineering

The traditional design approach of grabs and other bulk handling equipment consists of manufacturing and testing physical prototypes. A novel design approach is to use a co-simulation of MultiBody Dynamics (MBD) and Discrete Element Method (DEM), in which the virtual prototype of a new concept interacts with bulk solids. Therefore, this study develops and validates a full-scale co-simulation that models the grabbing process of cohesive and stress-history dependent iron ore. First, by executing in-situ measurements during the unloading of a vessel, grab-relevant bulk properties of the cargo, such as penetration resistance, are determined. Second, full-scale grabbing experiments are conducted in the cargo hold, which allows the process to be recorded in realistic operational conditions. Third, full-scale co-simulation is set up using the material model that has been calibrated based on an elasto-plastic adhesive contact model. Fourth, the co-simulation is validated by comparing its predictions to experimental data from various aspects, such as the force in cables and the torque in winches. The validated co-simulation proves that the stress-dependent behaviour of cohesive cargo as it interacts with the grab could be captured successfully. Valuable information such as a grab's kinematics and dynamics, as well as the porosity distribution of collected bulk solids, can be extracted from the simulation, supporting engineers to enhance the design and operation of equipment., Transport Engineering and Logistics, Offshore and Dredging Engineering, Rivers, Ports, Waterways and Dredging Engineering

Ship unloader grabs are usually designed using the manufacturer's in-house knowledge based on a traditional physical prototyping approach. The grab performance depends greatly on the properties of the bulk material being handled. By considering the bulk cargo variability in the design process, the grab performance can be improved significantly. A multi-objective simulation-based optimization framework is therefore established to include bulk cargo variability in the design process of grabs. The primary objective is to reach a maximized and consistent performance in handling a variety of iron ore cargoes. First, a range of bulk materials is created by varying levels of cohesive forces and plasticity in the elasto-plastic adhesive DEM contact model. The sensitivity analysis of the grabbing process to the bulk variability allowed three classes of iron ore materials to be selected that have significant influence on the product performance. Second, 25 different grab designs are generated using a random sampling method, Latin Hypercube Design, to be assessed as to their handling of the three classes of iron ore materials. Of this range of grab designs, optimal solutions are found using surrogate modelling-based optimization and the NSGA-II genetic algorithm. The optimization outcome is verified by comparing predictions of the optimization algorithm and results of DEM-MBD co-simulation. The established optimization framework offers a straightforward and reliable tool for designing grabs and other similar equipment., Transport Engineering and Logistics

Due to the high demand of iron ore products in the steel industry, they have the largest share in dry bulk trading per year, above coal and grains. Approximately 9000 Cape-size bulk carriers with capacities up to 400 000 tonnes (DWT) transport the annual demand of iron ore to destination ports. Grabs are employed extensively to unload iron ore from ship holds. A fast and reliable unloading process is required to maintain a minimized cost for port operators and to deliver iron ore products to customers on time. In practice, many factors, such as moisture, varying material properties over the cargo depth and grab’s dynamics, contribute in creating challenges for achieving the desired performance during the unloading process. A solution for improving the unloading process is to enhance the design of grabs by using simulation-based methods. This enables a higher mass of iron ore to be collected per grab cycle, thus minimizing the total unloading time of a bulk carrier. Virtual prototyping of grabs is a novel simulation-based method that allows for evaluating the design performance in an affordable way. The virtual prototype of a grab as it interacts with bulk material are co-simulated at full-scale by coupling two different solvers: Discrete Element Method (DEM) and MultiBody Dynamics (MBD). The co-simulation requires virtual crane operator, CAD model of grab connected to a crane, and calibrated DEM material model as inputs. Over the past decade, reliable DEM calibration procedures have been developed to model free-flowing bulk solids, such as iron ore pellets, sand and gravel. However, due to moisture content the majority of iron ore products show cohesive and stress-history dependent behaviours, which should be considered in the calibration procedure. Additionally, considering particle size and shape of such fine iron ore products, the extreme computation time of DEM simulations is a challenge to be solved. Furthermore, a grab is often used to handle a broad variety o, Transport Engineering and Logistics

An ideal unit of bulk transport or storage equipment is able to handle cohesive iron ore with consistent productivity. In practice, however, uncontrollable bulk property variations affect the productivity. Therefore, it is important to understand the effect of uncontrollable variations on the process. This study quantifies variability and interdependency of bulk property of a range of cohesive iron ore products. Three different laboratory tests relevant to storage and excavation processes are used. Using a multi-variate experimental plan, three influencing characteristics of iron ore – type, moisture content and consolidation state – are included. A stress-history dependent behavior is captured in both the shear and penetration tests, with the results being highly dependent on the pre-consolidation stress. The outcome of this study enables future research on minimizing the effect of uncontrollable bulk properties variability of iron ore and other cohesive materials in the design procedure of transport and storage processes., Transport Engineering and Logistics, Offshore and Dredging Engineering, Rivers, Ports, Waterways and Dredging Engineering