Shear flow DEM simulations of the elongated particles (rods and ellipsoids) to investigate the characteristics of our newly developed kinetic energy-based constitutive models.
Constitutive modelling of granular systems (2017-)

The critical importance of understanding the flow of granular materials in countless natural and industrial processes, has resulted in significant interdisciplinary research efforts for several decades. Of such efforts, one of the most significant contributions to the understanding of dry granular flows was the discovery of a relationship referred to as the rheology. This phenomenological model relates the internal friction coefficient (i.e. the shear stress to confining pressure ratio ), to the dimensionless inertial number which quantifies how rapid the material is flowing locally. Despite the many successful applications of the rheology, it loses accuracy for several key aspects of dry granular flows including application to dilute cases, accounting for particles with a finite stiffness (or soft particles), nonlocal and grain shape effects which have been highlighted as causing significant deviations from classical models. Elucidated from advances in non-local rheological models, is the roll of the particle velocity fluctuations often described as granular temperature, . Acknowledgement of the granular temperature for constitutive modelling efforts has now led to several recent remarkable proposals, mitigating many of the weaknesses of the traditional models. In this work through Discrete Element Method (DEM) simulations we are working on the development of new constitutive models based on the kinetic energy to address the shortcomings of the classical rheology.

This work is being conducted by Nathan.

Academic(s) involved: Sina and Yonghao

A strategy for modelling a full cycle of a typical powder-based AM process.
Modelling Additive Manufacturing Processes (2016-)

The powder-based additive manufacturing processes are promising manufacturing techniques that enable the rapid production of prototypes and lately for weight-sensitive/multi-functional parts at small volumes, with almost arbitrary complexity. The process builds the final parts layer-upon-layer by going through three main stages during each cycle: (1) deposition of a layer of fine powder () on a fabrication surface through powder spreading – e.g. in selective laser melting (SLM) – or spraying – e.g. in laser metal deposition (LMD); (2) laser heating at specific locations; (3) fusion of the powder grains through melting and solidification.

A lack of understanding of the impact of powder grain shape on this process, has forced the industry to place stringent requirements on powder characteristics (such as high grain sphericity and narrow size distribution) to increase process repeatability and improve the powders’ processability. These requirements also result in high cost of virgin powders and a high wastage of raw material due to lack of recyclability, and hence causing the technology readiness level (TRL) to remain low despite substantial advantages, such as the production of complex parts, mass customisation, and on-demand manufacturing. To overcome these difficulties, the Oak Ridge National Lab’s report identifies an urgent technological need for particle-scale (microscale) simulations of the process and integration of the simulation software with the powder characterisation data. Despite some progress in the past few years, the available models are of low-fidelity and various phenomena including vaporisation and het transfer within the bed are not properly considered. This project addresses this urgent need by (1) developing a novel procedure to integrate the powder characterisation data with the simulation software at temperatures exceeding 500°C equivalent to those experienced in the preheated build chamber, and (2) providing a validated computational framework for particle-scale modelling of a full cycle of the process.

This work is being conducted by Sorush.

Academic(s) involved: Sina

Mesoscale simulation framework based on a coupled DEM-PD theory.
Multi-physics Modelling of Erosive Impact of Particles on Wind Turbine Blades (2018-)

Wind energy has been developing rapidly over the past two decades. With 417 TWh generated, wind power covered 15% of the EU’s electricity demand in 2019 while in the same year, the sector has enjoyed over €19bn in new investments. In Q1 2020, offshore/onshore wind contributed to 64% of renewable – equivalent to 30% of the total – electricity generation in the UK. Large wind turbines consist of several blades with aerofoil shaped cross sections to generate lift and maintain the rotation. The blades are naturally subjected to high-speed winds and are bombarded by solid (e.g. hailstone) and/or liquid (e.g. rain droplets) “particles” depending on the installation site. Such impacts, increase the surface roughness by damaging the coatings which causes a substantial aerodynamic performance penalty as the friction drag increases and results in an earlier onset of stall. As the severity of erosion increases it will ultimately jeopardise the structural integrity of the blades resulting in unplanned downtime and high maintenance costs. A major issue during the design stage is the lack of predictive models to optimise the aerodynamic performance and materials concurrently. The motion of particles near the blades and the impact mechanics should ideally be considered during the design stage to guide the material selection and reduce impact probabilities through geometric optimisation. Such a predictive model will also determine the rate of mass removal allowing for scheduled maintenance which substantially lowers the associated costs. The key research question is therefore to understand the dynamical role of impinging particles in the erosion process, enabling quantitative prediction of the erosive impact of particles and mass removal rate from the surfaces.

This work is being conducted by Kinan and Khuram.

Academic(s) involved: Sina and Yonghao