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10 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Comparison of Eulerian Bin and Lagrangian Particle-Based Schemes in Simulations of Pi Chamber Dynamics and Microphysics

This paper presents a comparison of simulations applying either a traditional Eulerian bin microphysics or a novel particle-based Lagrangian approach to represent CCN activation and cloud droplet growth. The Eulerian microphysics solve the evolution equation for the spectral density function, whereas the Lagrangian approach follows computational particles referred to as superdroplets. Each superdroplet represents a multiplicity of natural droplets that makes the Lagrangian approach computationally feasible. The two schemes apply identical representation of CCN activation and use the same droplet growth equation; these make direct comparison between the two schemes practical. The comparison, the first of its kind, applies an idealized simulation setup motivated by laboratory experiments with the Pi Chamber and previous model simulations of the Pi Chamber dynamics and microphysics. The Pi Chamber laboratory apparatus considers interactions between turbulence, CCN activation, and cloud droplet growth in moist Rayleigh–Bénard convection. Simulated steady-state droplet spectra averaged over the entire chamber are similar, with the mean droplet concentration, mean radius, and spectral width close in Eulerian and Lagrangian simulations. Small differences that do exist are explained by the inherent differences between the two schemes and their numerical implementation. The local droplet spectra differ substantially, again in agreement with the inherent limitations of the theoretical foundation behind each approach. There is a general agreement between simulations and Pi Chamber observations, with simplifications of the CCN activation and droplet growth equation used in the simulations likely explaining specific differences.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Confronting the Challenge of Modeling Cloud and Precipitation Microphysics

In the atmosphere, microphysics refers to the microscale processes that affect cloud and precipitation particles and is a key linkage among the various components of Earth’s atmospheric water and energy cycles. The representation of microphysical processes in models continues to pose a major challenge leading to uncertainty in numerical weather forecasts and climate simulations. In this paper, the problem of treating microphysics in models is divided into two parts: i) how to represent the population of cloud and precipitation particles, given the impossibility of simulating all particles individually within a cloud, and ii) uncertainties in the microphysical process rates owing to fundamental gaps in knowledge of cloud physics. The recently-developed Lagrangian particle-based method is advocated as a way to address several conceptual and practical challenges of representing particle populations using traditional bulk and bin microphysics parameterization schemes. For addressing critical gaps in cloud physics knowledge, sustained investment for observational advances from laboratory experiments, new probe development, and next-generation instruments in space is needed. Greater emphasis on laboratory work, which has apparently declined over the past several decades relative to other areas of cloud physics research, is argued to be an essential ingredient for improving process-level understanding. More systematic use of natural cloud and precipitation observations to constrain microphysics schemes is also advocated. Because it is generally difficult to quantify individual microphysical process rates from these observations directly, this presents an inverse problem that can be viewed from the standpoint of Bayesian statistics. Following this idea, a probabilistic framework is proposed that combines elements from statistical and physical modeling. Besides providing rigorous constraint of schemes, there is an added benefit of quantifying uncertainty systematically. Finally, a broader hierarchical approach is proposed to accelerate improvements in microphysics schemes, leveraging the advances described in this paper related to process modeling (using Lagrangian particle-based schemes), laboratory experimentation, cloud and precipitation observations, and statistical methods.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Cloud droplet diffusional growth in homogeneous isotropic turbulence: bin microphysics versus Lagrangian super-droplet simulations

The increase in the spectral width of an initially monodisperse population of cloud droplets in homogeneous isotropic turbulence is investigated by applying a finite-difference fluid flow model combined with either Eulerian bin microphysics or a Lagrangian particle-based scheme. The turbulence is forced applying a variant of the so-called linear forcing method that maintains the mean turbulent kinetic energy (TKE) and the TKE partitioning between velocity components. The latter is important for maintaining the quasi-steady forcing of the supersaturation fluctuations that drive the increase in the spectral width. We apply a large computational domain (64 3 m 3 ), one of the domains considered in Thomas et al. (2020). The simulations apply 1 m grid length and are in the spirit of the implicit large eddy simulation (ILES), that is, with small-scale dissipation provided by the model numerics. This is in contrast to the scaled-up direct numerical simulation (DNS) applied in Thomas et al. (2020). Two TKE intensities and three different droplet concentrations are considered. Analytic solutions derived in Sardina et al. (2015), valid for the case when the turbulence integral timescale is much larger than the droplet phase relaxation timescale, are used to guide the comparison between the two microphysics simulation techniques. The Lagrangian approach reproduces the scalings relatively well. Representing the spectral width increase in time is more challenging for the bin microphysics because appropriately high resolution in the bin space is needed. The bin width of 0.5 µm is only sufficient for the lowest droplet concentration (26 cm -3 ). For the highest droplet concentration (650 cm -3 ), an order of magnitude smaller bin size is barely sufficient. The scalings are not expected to be valid for the lowest droplet concentration and the high-TKE case, and the two microphysics schemes represent similar departures. Finally, because the fluid flow is the same for all simulations featuring either low or high TKE, one can compare point-by-point simulation results. Such a comparison shows very close temperature and water vapor point-by-point values across the computational domain and larger differences between simulated mean droplet radii and spectral width. The latter are explained by fundamental differences in the two simulation methodologies, numerical diffusion in the Eulerian bin approach and a relatively small number of Lagrangian particles that are used in the particle-based microphysics.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

What Controls Crystal Diversity and Microphysical Variability in Cirrus Clouds?

Abstract Variability of ice microphysical properties like crystal size and density in cirrus clouds is important for climate through its impact on radiative forcing, but challenging to represent in models. For the first time, recent laboratory experiments of particle growth (tied to crystal morphology via deposition density) are combined with a state‐of‐the‐art Lagrangian particle‐based microphysics model in large‐eddy simulations to examine sources of microphysical variability in cirrus. Simulated particle size distributions compare well against balloon‐borne observations. Overall, microphysical variability is dominated by variability in the particles' thermodynamic histories. However, diversity in crystal morphology notably increases spatial variability of mean particle size and density, especially at mid‐levels in the cloud. Little correlation between instantaneous crystal properties and supersaturation occurs even though the modeled particle morphology is directly tied to supersaturation based on laboratory measurements. Thus, the individual thermodynamic paths of each particle, not the instantaneous conditions, control the evolution of particle properties.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Are turbulence effects on droplet collision–coalescence a key to understanding observed rain formation in clouds?

Rain formation is a critical factor governing the lifecycle and radiative forcing of clouds and therefore it is a key element of weather and climate. Cloud microphysics–turbulence interactions occur across a wide range of scales and are challenging to represent in atmospheric models with limited resolution. Based on past experiments and idealized numerical simulations, it has been postulated that cloud turbulence accelerates rain formation by enhancing drop collision–coalescence. We provide substantial evidence for significant impacts of turbulence on the evolution of cloud droplet size distributions and rain formation by comparing high-resolution observations of cumulus congestus clouds with state-of-the-art large-eddy simulations coupled with a Lagrangian particle-based microphysics scheme. Turbulent coalescence must be included in the model to accurately represent the observed drop size distributions, especially for drizzle drop sizes at lower heights in the cloud. Turbulence causes earlier rain formation and greater rain accumulation compared to simulations with gravitational coalescence only. The observed rain size distribution tail just above cloud base follows a power law scaling that deviates from theoretical scalings considering either a purely gravitation collision kernel or a turbulent kernel neglecting droplet inertial effects, providing additional evidence for turbulent coalescence in clouds. In contrast, large aerosols acting as cloud condensation nuclei (“giant CCN”) do not significantly impact rain formation owing to their long timescale to reach equilibrium wet size relative to the lifetime of rising cumulus thermals. Overall, turbulent drop coalescence exerts a dominant influence on rain initiation in warm cumulus clouds, with limited impacts of giant CCN.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Comparison of Eulerian Bin and Lagrangian Particle-Based Microphysics in Simulations of Nonprecipitating Cumulus

A single nonprecipitating cumulus congestus setup is applied to compare droplet spectra grown by the diffusion of water vapor in Eulerian bin and particle-based Lagrangian microphysics schemes. Bin microphysics represent droplet spectral evolution applying the spectral density function. In the Lagrangian microphysics, computational particles referred to as superdroplets are followed in time and space with each superdroplet representing a multiplicity of natural cloud droplets. The same cloud condensation nuclei (CCN) activation and identical representation of the droplet diffusional growth allow the comparison. The piggybacking method is used with the two schemes operating in a single simulation, one scheme driving the dynamics and the other one piggybacking the simulated flow. Piggybacking allows point-by-point comparison of droplet spectra predicted by the two schemes. Overall, the results show the impact of inherent limitations of the two microphysics simulation methods, numerical diffusion in the Eulerian scheme and a limited number of superdroplets in the Lagrangian scheme. Numerical diffusion in the Eulerian scheme results in a more dilution of the cloud upper half and thus smaller cloud droplet mean radius. The Lagrangian scheme typically has larger spatial fluctuations of droplet spectral properties. A significantly larger mean spectral width in the bin microphysics across the entire cloud depth is the largest difference between the two schemes. A fourfold increase of the number of superdroplets per grid volume and a twofold increase of the spectral resolution and thus the number of bins have small impact on the results and provide only minor changes to the comparison between simulated cloud properties.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Meshfree simulation and experimental validation of extreme thermomechanical conditions in friction stir extrusion

Abstract Friction stir extrusion (FSE) is a novel solid-phase processing technique that consolidates and extrudes metal powders, flakes, chips, or billets into high-performance parts by plastic deformation, which has the potential to save substantial processing time and energy. Currently, most studies on FSE are experimental and only a few numerical models have been developed to explain and predict the complex physics of the process. In this work, a meshfree simulation framework based on smoothed particle hydrodynamics (SPH) was developed for FSE. Unlike traditional grid-based methods, SPH is a Lagrangian particle-based method that can handle severe material deformations, capture moving interfaces and surfaces, and monitor the field variable histories explicitly without complicated tracking schemes. These aspects of SPH make it attractive for the FSE process, where in situ evolution of field variables is difficult to observe experimentally. To this end, a 3-D, fully thermomechanically coupled SPH model was developed to simulate the FSE of aluminum wires. The developed model was thoroughly validated by comparing the numerically predicted material flow, strain, temperature history, and extrusion force with experimental results for a certain set of process parameters. The validated SPH model can serve as an effective tool to predict and better understand the extreme thermomechanical conditions during the FSE process.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

pnnl/Lagrangian-Droplets

The Lagrangian Droplet cloud microphysics scheme uses high-resolution data of air velocity, temperature, and water vapor mixing ratio to drive particle-based simulations of cloud microphysics. The model was first implemented using a high-resolution Large Eddy Simulation of the Pi Chamber at Michigan Tech, which is small-scale convection chamber the generates clouds in a laboratory setting

Fierce, Laura [Pacific Northwest National Laborato↗
DOE OSTI2022

An Efficient Bayesian Approach to Learning Droplet Collision Kernels: Proof of Concept Using “Cloudy,” a New n -Moment Bulk Microphysics Scheme

The small-scale microphysical processes governing the formation of precipitation particles cannot be resolved explicitly by cloud resolving and climate models. Instead, they are represented by microphysics schemes that are based on a combination of theoretical knowledge, statistical assumptions, and fitting to data (“tuning”). Historically, tuning was done in an ad hoc fashion, leading to parameter choices that are not explainable or repeatable. Recent work has treated it as an inverse problem that can be solved by Bayesian inference. The posterior distribution of the parameters given the data—the solution of Bayesian inference—is found through computationally expensive sampling methods, which require over $\mathcal{O}$(10 5 ) evaluations of the forward model; this is prohibitive for many models. We present a proof of concept of Bayesian learning applied to a new bulk microphysics scheme named “Cloudy,” using the recently developed Calibrate-Emulate-Sample (CES) algorithm. Cloudy models collision-coalescence and collisional breakup of cloud droplets with an adjustable number of prognostic moments and with easily modifiable assumptions for the cloud droplet mass distribution and the collision kernel. The CES algorithm uses machine learning tools to accelerate Bayesian inference by reducing the number of forward evaluations needed to $\mathcal{O}$(10 2 ). It also exhibits a smoothing effect when forward evaluations are polluted by noise. In a suite of perfect-model experiments, we show that CES enables computationally efficient Bayesian inference of parameters in Cloudy from noisy observations of moments of the droplet mass distribution. In an additional imperfect-model experiment, a collision kernel parameter is successfully learned from output generated by a Lagrangian particle-based microphysics model.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Numerical investigation of inertial sphere impaction on a cylinder in subcritical flow

We investigated the accuracy of a Lagrangian scheme for simulating both the motion and wall impaction of neutrally buoyant inertial spheres (8.85 ≤ St ≤ 18.04) moving in the subcritical regime (Re = 10,972 and 22,366) of flows past a circular cylinder. The accuracy of the eddy-resolving simulations of the flow field was verified based on available data at nearly comparable flow conditions. The accuracy of the eddy-resolving simulations of the flow field was verified based on available data at nearly comparable flow conditions. The accuracy of the particle tracking scheme was verified with respect to corresponding experimental data in a laboratory flume. Particle dispersion en route to the circular cylinder is mostly driven by the flow disturbance from the particle injector tube. The trends in collision rates are largely explained by the rate of particle entertainment into the boundary layer around the cylinder and the inertial condition of the spheres in this proximity region—both of which can be better assessed by post-processing of the numerical outcomes. Here, the primary incentive to undertake this work was the need for validating a particle-based method for quantifying the hydraulic stressors leading to bodily damage of fish passing flows influenced by hydraulic infrastructure.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗