We address the computational challenges of large-scale geospatial mapping with Gaussian process (GP) regression by performing localized computations rather than processing the entire map simultaneously. Traditional approaches to GP regression often involve computational and storage costs that either scale with the number of measurements, or with the spatial extent of the mapped area, limiting their scalability for real-time applications. Our method places a global grid of finite-support basis functions and restricts computations to a local subset of the grid 1) surrounding the measurement when the map is updated, and 2) surrounding the query point when the map is queried. This localized approach ensures that only the relevant area is updated or queried at each timestep, significantly reducing computational complexity while maintaining accuracy. Unlike many existing methods, which suffer from boundary effects or increased computational costs with mapped area, our localized approach avoids discontinuities and ensures that computational costs remain manageable regardless of map size. This approximation to GP mapping provides high accuracy with limited computational budget for the specialized task of performing fast online map updates and fast online queries of large-scale geospatial maps. It is therefore a suitable approximation for use in real-time applications where such properties are desirable, such as real-time simultaneous localization and mapping (SLAM) in large, nonlinear geospatial fields. We show on experimental data with magnetic field measurements that our algorithm is faster and equally accurate compared to existing methods, both for recursive magnetic field mapping and for magnetic field SLAM.

Deep sea mining refers to the mining of valuable mineral resources from the deep ocean floor. Given the complex and fragile nature of deep-sea ecosystems, adopting an interdisciplinary and holistic approach is crucial to ensure the sustainable and responsible development of deep-sea mining (DSM) operations. This includes work related to the assessment of potential environmental impacts where physical, chemical, and biological characteristics of the target area are studied along with potential short-term and long-term effects on the surrounding ecosystems. These effects will be mining system dependent. Stakeholder engagement is essential. There are however knowledge gaps related to the deep-sea ecosystems and their interconnectedness, biodiversity, ecosystem dynamics and both the potential impacts from a single operation and cumulative impacts of the mining activities, as well as the mining systems themselves and the characteristics of the deposits. Collaboration between marine biologists, oceanographers, geologists, engineers and other relevant disciplines is essential to gain comprehensive insights. Closing these gaps would enable the development and implementation of a robust regulatory framework at both national and international levels to govern potential deep-sea mining operations. Monitoring and enforcement mechanisms must also be put in place to ensure compliance with the not-yet-developed set of standards. Multiscale adaptive management approaches where different temporal- and spatial scales are taken into consideration and where scientific knowledge, stakeholder engagement, robust regulations, and responsible practices are integrated, are the prerequisite for future responsible extraction of mineral resources from the ocean floor. This chapter gives an overview of topics relevant and needed for a proper multiscale marine mineral management. Its focus on the water column is restricted to vertical transportation and the impact of plume resettlement on biogeochemical processes.

Turbidity flows are known to be affected by the density difference between sediment plumes and the surrounding water. However, besides density, other factors could lead to changes in flow propagation. Such a factor is the presence of suspended organic matter. Recently, it was found that flocculation does occur within plumes upon release of a sediment/organic matter mixture in a lock exchange flume. In the present study, mineral sediment (illite clay) was released into the outflow compartment containing water and synthetic organic matter (polyacrylamide flocculant). Even though the density of water was barely affected by the presence of flocculant, flow head velocity was observed to be larger in the presence of flocculant than without. Samples taken at different positions in the flume indicated that flocs were created during the small current propagation time (about 30–60 s) and that their sizes were larger with higher flocculant dosage. The size of flocs depended on their positions in the flow: flocs sampled in the body part of the flow were larger than the ones sampled at the bottom. All these properties are discussed as a function of sediment–flocculant interactions.

The insights gained from deep-sea mining (DSM) research regarding sediment dynamics can be utilized to better predict turbidity plumes in shallow marine environments. Small-scale lab experiments can replicate deep-sea conditions effectively, offering an ideal model system to study turbidity currents, given the reduced hydrodynamics and low biota present in the deep sea. DSM operations involve the deployment of a Polymetallic Nodule Mining Tool (PNMT) that collects ore and discharges excess water and sediments. Organic matter, bound to mineral clay as floes, is a key driver of sediment transport in the deep sea. Understanding the dispersal and settling patterns of sediments, and the likelihood of flocculation occurring in DSM activities, can be generalized and applied to turbid flows in shallow water areas. Laboratory experiments demonstrate that the interaction between organic matter, mineral clay, and floes within turbidity currents, results in the reduction of their dispersion. Alongside this, factors like shear rate and sediment concentration significantly influence both floe growth, size and settling velocities. Combining these results with real-time data on sediment concentration, particle size distribution, turbidity, and flow dynamics can be helpful to make dredging decisions, reduce the environmental disruption, and guide dredging equipment selection. By understanding the factors that influence sediment flocculation, deposition, and resuspension, we can design engineered solutions to mitigate the impact of turbidity current.

Self-amplifying density waves in hydraulic transport pipelines is a scarcely researched topic. Density waves are in essence the result of a spatial redistributing effect and clustering of solids in hydraulic transport pipelines. Self-amplifying density waves are very undesirable for practical applications, as these waves increasing the risk of pipeline blockages. The two available experimental studies (Talmon et al., 2007; Matoušek and Krupička, 2013) report conflicting properties of the density waves, such as wave length and wave celerity. This new experimental research aims to shed light on the reported differences, by broadly varying particle size and concentration in a new dedicated experiment. The main highlight of this research is that two separate mechanisms were identified that can cause density waves, and Talmon et al. (2007) and Matoušek and Krupička (2013) in hindsight were studying the two different mechanism respectively. Both wave type mechanisms come into effect at mixture velocities close to the deposit limit velocity, and require a stationary bed layer to initiate. The first mechanism is caused by an imbalance of erosion and sedimentation of the bed layer, which is predominant for fine sand particles (∼242μm and ∼308μm in this research). The second mechanism occurs when the bed layer starts sliding, instead of being eroded, and is specific for larger sand sizes (∼617μm and ∼1.08mm in this research). These two mechanisms are clearly distinguishable, having different wave lengths, celerity, amplitudes and amplification rates. The results also show a clear relationship between the mean concentration of a density wave, the wave amplitude and wave celerity specific for each of the two mechanisms.

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.

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.

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.

The chapter gives an overview of the sediment dispersion generated by the mining process. Within the field of dredging engineering, ample experience is available regarding equipment, turbidity generated by equipment, and sediment transport processes. High up the environmental impact mitigation hierarchy are avoidance and minimization. That is where engineering can provide (part of) the solution. It is our aim to predict and consider how we can improve the mining process and equipment. Within this context, our focus is on those processes that are likely to take place close to the seabed. On the one hand, our work focuses on the prediction and reduction of the amount of sediment that might get suspended. On the other hand, considering the conditions under which the suspended sediment might be released in the most optimal way to reduce dispersion, we have performed and analysed small-scale and full-scale laboratory experiments of a hydraulic collector design and various dynamic sedimentation experiments.

In this paper, model-based systems engineering (MBSE) and discrete event simulation (DES) are combined to assess the performance of an offshore production system at an early stage. Various systems engineering tools are applied to an industrial case concerning the retrieval of deep-sea minerals, and a simulation engine is developed to calculate the annual production output. A mean production of 1 Million tonnes of ore per year is estimated for an operation in the Norwegian Sea using Monte Carlo simulation. Depending on the limiting design wave height of the marine operations, the estimated production output ranges from 280,000 tonnes to 1.8 Million tonnes per year. The constrained parameter of the production system is particularly the wave height operational limit of the ship-to-ship transfer operation. We present the learning outcome from applying MBSE and DES to this case and discuss important aspects for improved performance.

Accurately estimating the positions of multi-agent systems in indoor environments is challenging due to the lack of Global Navigation Satelite System (GNSS) signals. Noisy measurements of position and orientation can cause the integrated position estimate to drift without bound. Previous research has proposed using magnetic field simultaneous localization and mapping (SLAM) to compensate for position drift in a single agent. Here, we propose two novel algorithms that allow multiple agents to apply magnetic field SLAM using their own and other agents' measurements.Our first algorithm is a centralized approach that uses all measurements collected by all agents in a single extended Kalman filter. This algorithm simultaneously estimates the agents' position and orientation and the magnetic field norm in a central unit that can communicate with all agents at all times. In cases where a central unit is not available, and there are communication drop-outs between agents, our second algorithm is a distributed approach that can be employed.We tested both algorithms by estimating the position of magnetometers carried by three people in an optical motion capture lab with simulated odometry and simulated communication dropouts between agents. We show that both algorithms are able to compensate for drift in a case where single-agent SLAM is not. We also discuss the conditions for the estimate from our distributed algorithm to converge to the estimate from the centralized algorithm, both theoretically and experimentally. Our experiments show that, for a communication drop-out rate of 80%, our proposed distributed algorithm, on average, provides a more accurate position estimate than single-agent SLAM. Finally, we demonstrate the drift-compensating abilities of our centralized algorithm on a real-life pedestrian localization problem with multiple agents moving inside a building.

A convex pattern surface has been proposed and optimized to reduce sliding wear of bulk handling equipment by adjusting the flow behaviour of bulk material. This study aims at modelling the surface deformation of the convex pattern sample to investigate how effectively the sample reduces sliding wear. Archard wear model and a deformable geometry technique are combined to capture the sample deformation. A short-time laboratory wear experiment is performed as a benchmark to validate the numerical model. The simulation resutls indicate that there is a linear relation between the wear volume of a plain sample and the simulated revolutions, while the convex pattern sample has a quadratic trend. The wear distribution displays that the convex pattern accounts for the majority of wear of the sample. The contact behaviour demonstrates that the convex pattern facilitates the rolling of particles, resulting in the reduction of sliding distance. The numerical results indicate that the deformed convex pattern sample leads to lower overall sliding wear than a plain sample, although its effectiveness weakens as wear evolves.

To date, hydraulic collection is the most widely considered technology in polymetallic-nodule mining, since there is no direct contact between hydraulic collectors and ocean floor. To construct a hydraulic collector that results in the least sediment disturbance, it is critical to develop an insightful understanding of the interaction between the collector and sediment bed. To this end, we conducted a set of small-scale experiments in which several operational conditions were tested, delivering the first quantitative data for sediment erosion resulting from a hydraulic collector driving over a sand bed. This paper presents and discusses the experimental results and observations. It is found that the collector’s forward velocity is inversely proportional to the bed-sediment erosion depth, since the bed is exposed to the flow for a longer time when the collector drives slower and vice versa. In contrast, an increased jet velocity leads to a larger erosion depth. Furthermore, when the collector underside is nearer to the sediment bed, a larger sediment layer is exposed to the water flow, resulting in a larger erosion depth. Finally, the experimental results show that collector water jets strike the sediment bed under an inclined angle, destabilizing the upper sediment layer and consequently dragging sediment particles along toward the collection duct and behind the collector head. This study improves the predictability of sediment erosion created by Coandă-effect-based collectors, which is a crucial asset to optimize the collector design and decrease the extent of the associated sediment plumes.

We present an effective design of a hydraulic, polymetallic nodule collector, which fundamentally depends on the Coandă effect in harvesting nodules. The design was first developed based on 2D numerical simulations conducted using a computational fluid dynamics tool, ANSYS FLUENT. Following that, the design was tested in full-scale experiments, which provided insights into the collection efficiency of the collector and confirmed its functionality and effectiveness. The latter means, in the context of deep sea mining, high effective pick-up of nodules, with minimum sediment disturbance. Our observations indicate that our design hardly disturbs the tested sediment bed. The experimental results show that a higher jet velocity leads to a higher pick-up efficiency. Two forward velocities were tested and the higher forward velocity led to a lower pick-up efficiency. It is revealed that the available time for the nodules to respond to the pressure gradient under the collector is of great importance; if the available time is not sufficient, the nodules will not be picked-up even if the pressure gradient is adequate. The clearance under the rear cowl of the collection duct is found to play a major influential role in the collection process; a smaller bottom clearance results in a higher pick-up efficiency.

We have developed and investigated a hydrodynamic model of Deep-Sea Mining (DSM) collector turbidity flows that captures sediment particle aggregation and breakup. Flocculation is expected to have a significant impact on determining the spread patterns of the turbidity flows and the resulting turbidity currents. The recently validated drift-flux model by Elerian et al. (2022) has been coupled to the Population Balance Equation (PBE) for modelling real-life discharge scenarios. This advanced approach accounts for the dynamics of flocculation and offers a comprehensive simulation of discharge systems. We hypothesize that this will produce a more accurate representation of DSM turbidity flows in the near-field region, where the turbulence mixing is expected to be the highest. Particular emphasis is placed on the settling velocity closure, as the flocs that form are porous and have a complex geometry. The flocculation parameters are calibrated using the experiments of Gillard et al. (2019). Finally, we investigate the effect of flocculation in the near-field region by numerically solving the new model in a computational domain of the near-field region. The results indicate that aggregation is the primary mechanism, however, it does not have a visible impact on the turbidity flow in the immediate vicinity, but it is likely to have a substantial effect on the far-field region.

We present a computationally efficient algorithm for using variations in the ambient magnetic field to compensate for position drift in integrated odometry measurements (dead-reckoning estimates) through simultaneous localization and mapping (SLAM). When the magnetic field map is represented with a reduced-rank Gaussian process (GP) using Laplace basis functions defined in a cubical domain, analytic expressions of the gradient of the learned magnetic field become available. An existing approach for magnetic field SLAM with reduced-rank GP regression uses a Rao-Blackwellized particle filter (RBPF). For each incoming measurement, training of the magnetic field map using an RBPF has a computational complexity per time step of O(NpN2m), where Np is the number of particles, and Nm is the number of basis functions used to approximate the Gaussian process. Contrary to the existing particle filter-based approach, we propose applying an extended Kalman filter based on the gradients of our learned magnetic field map for simultaneous localization and mapping. Our proposed algorithm only requires training a single map. It, therefore, has a computational complexity at each time step of O(N2m). We demonstrate the workings of the extended Kalman filter for magnetic field SLAM on an open-source data set from a foot-mounted sensor and magnetic field measurements collected onboard a model ship in an indoor pool. We observe that the drift compensating abilities of our algorithm are comparable to what has previously been demonstrated for magnetic field SLAM with an RBPF.

Polymetallic nodules are potato-sized rock accretions that form on vast areas of the abyssal plains of the global ocean. These nodules are rich in commercially precious metals, such as nickel, cobalt and copper, making them a target for potential future deep-sea exploitation. Generally, polymetallic nodules are partially buried in the seabed sediment, which is predominantly composed of clay. Among the existing mechanisms for mining polymetallic nodules (mechanical, hydraulic and hybrid), hydraulic collecting is deemed the most suitable technology in deep sea mining. This is primarily because hydraulic collecting hardly involves interaction with the seabed during the collection process (Agarwal et al., 2012); the collector generates a pressure gradient to harvest the nodules, thus substantially reducing the associated disturbance to the seabed.

Renewable energy installations and energy storage solutions require significant quantities of critical raw materials such as nickel, cobalt and rare earth metals. The supply chains of these raw materials face many difficulties, such as the continuous decrease of mineral ore grades on land. In view of these complications, the motivation to search for new resources has grown, with the deep sea being seen as a potential source of these minerals. Polymetallic nodule mining generates turbidity currents, which could negatively impact the deep-sea environment. For that reason, we investigate this type of current experimentally and numerically in order to characterize the generated turbidity current. Various non-cohesive sediment types, i.e., different particle sizes, and different concentrations are tested using a lock-exchange set-up. Three sediment types (glass beads, silica sand and a 50/50 blend of glass beads and silica sand) with seven initial sediment concentrations are examined. Additionally, for the numerical work, a drift–flux modelling approach is used to simulate the performed lock-exchange experiments. The results show that the front velocities of the currents resulting from the three sediment types increases with increasing initial concentrations inside the lock regardless. Moreover, using the same initial concentration, the difference in front velocities between the generated currents of the three sediment types decreases as the initial concentration increases. When using an initial volumetric concentration of 2.5% and 3%, the difference in front velocities between the generated current of the three sediment types vanishes. Finally, by comparing the numerical and experimental results, the drift–flux model is proven to be a reliable numerical model for predicting the current.

There is an expected increase in demand of critical raw materials, e.g. nickel and cobalt, mainly driven by the shift towards renewable energy solutions and the increase in production of electric vehicles. That trend has raised the interests for the mining of polymetallic nodules. The most extensive nodule depostions are found on the abyssal plains of the deep seas. In spite of their economic interest, concerns have been raised regarding the environmental impact of deep sea mining.

Over the past decade, deep-sea mining (DSM) has received renewed interest due to scarcity of raw materials. Deep-sea mining has been spurred by the need for critical resources to support growing populations, urbanization, high-tech applications and the development of a green energy economy. Nevertheless, an improved understanding of how mining activities will affect the deep-sea environment is required to obtain more accurate assessment of the potential environmental impact. In that regard, the sediment plumes that are generated by the mining activity have received the highest concern, as these plumes might travel for several kilometers distance from the mining activity. Various plume sources are identified, of which the most profound are those generated by the
excavation and collection process of the seafloor mining tool and the discharge flow to be released from the surface operation vessel after initial dewatering of the ore. In this review, we explore the physical processes that govern plume dispersion phenomena (focusing in the main on benthic plumes), discuss the state of the art in plume dispersion analysis and highlight what lessons can be learned from shallow water applications, such as dredging, to better predict and reduce the spread and impact of deep-sea mining plumes.