Regularization of the Lagrangian point force approximation for deterministic discrete particle simulations

Modulation of homogeneous and isotropic turbulence by sub-Kolmogorov particles: Impact of particle field heterogeneity

“…Some of them are described by Capecelatro and Desjardins (2013). Diffusion operation might also be necessary to deal with finer grids (Poustis et al, 2019). Maxey et al (1997) proposed a "narrow envelope function" in the form ∆(x) = (2πσ 2 ) −3/2 exp(−x 2 /2σ 2 ), which was used by other works (Capecelatro and Desjardins, 2013; Zamansky et al, 2014).…”

The discrete Green's function paradigm for two-way coupled Euler–Lagrange simulation

“…Interestingly, the undisturbed fluid velocity can be accurately estimated regardless of the choice of interpolation/projection stencil as previously alluded to in Esmaily & Horwitz (2018). While Gaussian schemes may offer certain advantages to stability or smoothing of the flow field near the particle (Poustis et al 2019), it seems low-memory access interpolation/projection may still be advantageous since these EL data-transfer methods still allow accurate computation of the undisturbed fluid velocity and hence the energetics of the system will be consistent with the chosen drag law (Mehrabadi et al 2018;Horwitz & Mani 2020). From the standpoint of storage, it may also be favourable to use low-memory access projection/interpolation stencils to reduce the cost of storing the discrete Green's function.…”

“…Within the past five years, several methods have been proposed to estimate the undisturbed fluid velocity for momentum two-way coupled EL methods (Gualtieri et al 2015 Poustis et al 2019) as well as the undisturbed temperature for thermally two-way coupled EL methods (Horwitz et al 2017;Liu et al 2019). These methods have shown similar accuracy in the context of laminar verification problems while they have different ranges of applicability.…”

“…These methods have shown similar accuracy in the context of laminar verification problems while they have different ranges of applicability. Some of these methods are properly suited for low particle Reynolds number (Gualtieri et al 2015;Ireland & Desjardins 2017) while others have been extended to finite Reynolds number (Esmaily & Horwitz 2018;Balachandar et al 2019;Fukada et al 2019;Poustis et al 2019). While some methods are suited to isotropic grids , 2018Horwitz et al 2017), other methods can be employed on more general grid configurations (Gualtieri et al 2015;Ireland & Desjardins 2017;Esmaily & Horwitz 2018;Balachandar et al 2019;Liu et al 2019;Poustis et al 2019).…”

Quantifying the errors of the particle-source-in-cell Euler-Lagrange method

“…Assumptions are required to calculate M, the transfer of momentum from the particles to the fluid. The particle-source-in cell (PSIC) model of Crowe et al [8], although proposed in the 1970's, is still widely used to address this issue, owing to its relative simplicity compared to the more recent approaches that consist in convoluting the momentum contribution with a smooth kernel [7,20,4,25,12]. In a computational cell K, the momentum transfer term as defined in the PSIC model reads as…”