Data-driven model order reduction for granular media

Wallin, Erik; Servin, Martin

doi:10.1007/s40571-020-00387-6

Cited by 9 publications

(10 citation statements)

References 23 publications

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“…An alternative to improving the active zone model by analytical means is to take a data-driven approach. Following Wallin [50], it is possible to train a model, from resolved reference simulations, to rapidly predict the digging resistance and the flow field in a granular bed from a digging tool of particular 3D shape without explicitly defining any cutting edges, direction vectors or teeth. Potentially this can be used for predicting the shape and mechanical strength of the active zone with higher precision and generality, and possibly also the soil displacements.…”

Section: Discussionmentioning

confidence: 99%

A multiscale model of terrain dynamics for real-time earthmoving simulation

Servin

Berglund

Nystedt

2021

Adv. Model. and Simul. in Eng. Sci.

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A multiscale model for real-time simulation of terrain dynamics is explored. To represent the dynamics on different scales the model combines the description of soil as a continuous solid, as distinct particles and as rigid multibodies. The models are dynamically coupled to each other and to the earthmoving equipment. Agitated soil is represented by a hybrid of contacting particles and continuum solid, with the moving equipment and resting soil as geometric boundaries. Each zone of active soil is aggregated into distinct bodies, with the proper mass, momentum and frictional-cohesive properties, which constrain the equipment’s multibody dynamics. The particle model parameters are pre-calibrated to the bulk mechanical parameters for a wide range of different soils. The result is a computationally efficient model for earthmoving operations that resolve the motion of the soil, using a fast iterative solver, and provide realistic forces and dynamic for the equipment, using a direct solver for high numerical precision. Numerical simulations of excavation and bulldozing operations are performed to test the model and measure the computational performance. Reference data is produced using coupled discrete element and multibody dynamics simulations at relatively high resolution. The digging resistance and soil displacements with the real-time multiscale model agree with the reference model up to 10–25%, and run more than three orders of magnitude faster.

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Section: Discussionmentioning

confidence: 99%

A multiscale model of terrain dynamics for real-time earthmoving simulation

Servin

Berglund

Nystedt

2021

Adv. Model. and Simul. in Eng. Sci.

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“…Even with pseudo-particles as coarse as a loader bucket, the number of particles in a silo or stockpile asset may exceed what can be simulated in real time with present hardware. To guarantee the real-time performance of the particle dynamics, we employed data-driven model order reduction [18]. The idea was to run numerous DEM simulations in advance, covering the relevant state-space, as well as possibly using authentic CAD models and control signals for in-and out-flow to the asset.…”

Section: Digital Twin As a Distributed Particle Simulationmentioning

confidence: 99%

“…A virtual stockpile asset was created using AGX Dynamics for the particle simulation. To accelerate the simulations to real-time performance, a reducedorder model was trained from ground truth simulation data, as described in [18], with a tracking accuracy of 10%. The storage asset was connected to an ABB 800xA process simulator [27] using OPC [28] for receiving a discharge control signal and for exchanging macro-tracking data.…”

Section: Integration Testmentioning

confidence: 99%

“…For fast simulation, we used the nonsmooth discrete element method, which allows large time-step integration [17]. To achieve real-time performance in large processes, a data-driven model order reduction technique [18] was employed. The combination of the two models in an executable application, which can be part of a distributed system like a digital twin, we call a granular surrogate.…”

Section: Introductionmentioning

confidence: 99%

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Digital Twins with Distributed Particle Simulation for Mine-to-Mill Material Tracking

2021

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Systems for transport and processing of granular media are challenging to analyse, operate and optimise. In the mining and mineral processing industries, these systems are chains of processes with a complex interplay among the equipment, control and processed material. The material properties have natural variations that are usually only known at certain locations. Therefore, we explored a material-oriented approach to digital twins with a particle representation of the granular media. In digital form, the material is treated as pseudo-particles, each representing a large collection of real particles of various sizes, shapes and mineral properties. Movements and changes in the state of the material are determined by the combined data from control systems, sensors, vehicle telematics and simulation models at locations where no real sensors could see. The particle-based representation enables material tracking along the chain of processes. Each digital particle can act as a carrier of observational data generated by the equipment as it interacts with the real material. This make it possible to better learn the material properties from process observations and to predict the effect on downstream processes. We tested the technique on a mining simulator and demonstrated the analysis that can be performed using data from cross-system material tracking.

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“…For fast simulation we use the nonsmooth discrete element method, which allow large time-step integration Servin, Wang, Lacoursiére, and Bodin]. To achieve real-time performance in large processes, a data-driven model order reduction technique [Wallin and Servin(2021)] is employed. The combination of the two models in an executable application, that can be part of a distributed system like a digital twin, we call a granular surrogate.…”

Section: Introductionmentioning

confidence: 99%

Digital twins with distributed particle simulation for mine-to-mill material tracking

Servin¹,

Vesterlund²,

Wallin³

2021

Preprint

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Systems for transport and processing of granular media are challenging to analyse, operate and optimise. In the mining and mineral processing industries these systems are chains of processes with complex interplay between the equipment, control, and the processed material. The material properties have natural variations that are usually only known at certain locations. Therefore, we explore a material-oriented approach to digital twins with a particle representation of the granular media. In digital form, the material is treated as pseudo-particles, each representing a large collection of real particles of various sizes, shapes and, mineral properties. Movements and changes in the state of the material are determined by the combined data from control systems, sensors, vehicle telematics, and simulation models at locations where no real sensors can see. The particle-based representation enables material tracking along the chain of processes. Each digital particle can act as a carrier of observational data generated by the equipment as it interacts with the real material. This makes it possible to better learn material properties from process observations, and to predict the effect on downstream processes. We test the technique on a mining simulator and demonstrate analysis that can be performed using data from cross-system material tracking.

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Data-driven model order reduction for granular media

Cited by 9 publications

References 23 publications

A multiscale model of terrain dynamics for real-time earthmoving simulation

A multiscale model of terrain dynamics for real-time earthmoving simulation

Digital Twins with Distributed Particle Simulation for Mine-to-Mill Material Tracking

Digital twins with distributed particle simulation for mine-to-mill material tracking

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