The evaluation of the flow properties of biomass powders is essential for the design of handling systems within a thermochemical valorization context. The Discrete Element Method (DEM) is a valuable tool for simulating the bulk behavior of granular materials that has rarely been used for biomass feedstocks. This work focuses on the numerical investigation of the flow of raw and torrefied biomass particles in a loose and dynamic conditioning using a rotating drum. The relevance of DEM parameters calibrated using bulk experiments (angle-of-repose, bulk density, retainment ratio) is tested by comparison with experimental data obtained using a rotating drum system. The calibrated DEM material model considers the elongated, submillimetric and cohesive nature of the biomass powder. Several flowability descriptors (Upper Angle of Stability, size of avalanches, fraction of revolution to trigger events and irregularity of the free surface) are evaluated using both experimental data and DEM simulations. DEM results reproduced well the experimental trends and distinguished between the different cohesive extent of the samples. DEM is therefore a relevant technique for assessing flowability of biomass powders in a non-consolidated dynamic flow. This paves the way for investigating the effects of particle characteristics on bulk flow, which are briefly discussed.

Biomass feeding problems greatly hinder the industrialization of entrained-flow gasification systems for production of 2nd generation biofuels. Appropriate DEM modelling could allow engineers to design solutions that overcome these flow problems. This work shows the application of a DEM calibration framework to produce a realistic, calibrated and efficient material model for lignocellulosic biomass. A coarse (500–710 µm) and a fine (200–315 µm) sieving cut of milled poplar were used in this study. The elongated shape and the cohesive behavior were respectively simulated using a coarse-grained multisphere approach and a cohesive SJKR contact model. Measurements of three physical responses (angle-of-repose, bulk density, a retainment ratio) allowed calibration of the sliding (µ_s) and rolling friction (µ_r) coefficients and the cohesion energy density (CED). Using a statistical analysis, the most influential calibration parameters for each bulk response were identified. A Non-Dominated Sorting Genetic Algorithm was used to solve the calibration multi-objective optimization problem. Several sets of optimal solutions reproduced accurately the three physical responses and the experimental shear responses were closely reproduced by simulations for the population of coarse particles. The DEM calibration framework studied here aims to produce material models useful for assessing flow behavior and equipment interaction for biomass particles.

The buckling properties of thin-walled structures are sensitive to different sources of imperfections, among which the geometric imperfections are of paramount importance. This work contributes to the methodology of shell buckling analysis with respect to the following aspects: first, we propose an isogeometric analysis framework for the buckling analysis of shell structures which naturally eliminates the geometric discretization errors; second, we introduce a parameter-free Nitsche-type formulation for thin shells at large deformations that weakly enforces coupling constraints along trimmed boundaries. In combination with the finite cell method, the proposed conceptual modeling and analysis framework is able to handle engineering-related shell structures; and third, we introduce a NURBS modeling of measured geometric imperfection fields, which is much closer to the true imperfection shape compared to the classically used faceted FE models. We demonstrate with a number of benchmark problems and engineering models that our proposed framework is able to fully compete with established and highly sophisticated finite element formulations but at a significant higher level of accuracy and reliability of the analysis results.

In this research, a universal framework for automated calibration of microscopic properties of modeled granular materials is proposed. The proposed framework aims at industrial scale applications, where optimization of the computational time step is important. It can be generally applied to all types of DEM simulation setups. It consists of three phases: data base generation, parameter optimization, and verification. In the first phase, DEM simulations are carried out on a multi-dimensional grid of sampled input parameter values to generate a database of macroscopic material responses. The database and experimental data are then used to interpolate the objective functions with respect to an arbitrary set of parameters. In the second phase, the Non-dominated Sorting Genetic Algorithm II (NSGA-II) is used to solve the calibration multi-objective optimization problem. In the third phase, the DEM simulations using the results of the calibrated input parameters are carried out to calculate the macroscopic responses that are then compared with experimental measurements for verification and validation. The proposed calibration framework has been successfully demonstrated by a case study with two-objective optimization for the model accuracy and the simulation time. Based on the concept of Pareto dominance, the trade-off between these two conflicting objectives becomes apparent. Through verification and validation steps, the approach has proven to be successful for accurate calibration of material parameters with the optimal simulation time.

This research aims at developing a universal methodology for automated calibration of microscopic properties of modelled granular materials. The proposed calibrator can be applied for different experimental set-ups. Two optimization approaches: (1) a genetic algorithm and (2) DIRECT optimization, are used to identify discrete element method input model parameters, e.g., coefficients of sliding and rolling friction. The algorithms are used to minimize the objective function characterized by the discrepancy between the experimental macroscopic properties and the associated numerical results. Two test cases highlight the robustness, stability, and reliability of the two algorithms used for automated discrete element method calibration with different set-ups.