Showing papers on "Convection published in 2018"

Could Machine Learning Break the Convection Parameterization Deadlock

Abstract: Modeling and representing moist convection in coarse-scale climate models remains one of the main bottlenecks of current climate simulations. Many of the biases present with parameterized convection are strongly reduced when convection is explicitly resolved (in cloud resolving models at high spatial resolution ~ a kilometer or so). We here present a novel approach to convective parameterization based on machine learning over an aquaplanet with prescribed sea surface temperatures. The machine learning is trained over a superparameterized version of a climate model in which convection is resolved by an embedded 2D cloud resolving models. The machine learning representation of convection, called Cloud Brain (CBRAIN) replicates many of the convective features of the superparameterized climate model, yet reduces its inherent stochasticity. The approach presented here opens up a new possibility and a first step towards better representing convection in climate models and reducing uncertainties in climate predictions.

Using Machine Learning to Parameterize Moist Convection: Potential for Modeling of Climate, Climate Change, and Extreme Events

Abstract: The parameterization of moist convection contributes to uncertainty in climate modeling and numerical weather prediction. Machine learning (ML) can be used to learn new parameterizations directly from high-resolution model output, but it remains poorly understood how such parameterizations behave when fully coupled in a general circulation model (GCM) and whether they are useful for simulations of climate change or extreme events. Here, we focus on these issues using idealized tests in which an ML-based parameterization is trained on output from a conventional parameterization and its performance is assessed in simulations with a GCM. We use an ensemble of decision trees (random forest) as the ML algorithm, and this has the advantage that it automatically ensures conservation of energy and non-negativity of surface precipitation. The GCM with the ML convective parameterization runs stably and accurately captures important climate statistics including precipitation extremes without the need for special training on extremes. Climate change between a control climate and a warm climate is not captured if the ML parameterization is only trained on the control climate, but it is captured if the training includes samples from both climates. Remarkably, climate change is also captured when training only on the warm climate, and this is because the extratropics of the warm climate provides training samples for the tropics of the control climate. In addition to being potentially useful for the simulation of climate, we show that ML parameterizations can be interrogated to provide diagnostics of the interaction between convection and the large-scale environment.

Magnetic nanofluid flow and convective heat transfer in a porous cavity considering Brownian motion effects

Abstract: In the present article, the improvement of nanofluid heat transfer inside a porous cavity by means of a non-equilibrium model in the existence of Lorentz forces has been investigated by employing control volume based finite element method Nanofluid properties are estimated by means of Koo-Kleinstreuer-Li The Darcy-Boussinesq approximation is utilized for the nanofluid flow Roles of the solid-nanofluid interface heat transfer parameter Nhs, Hartmann number Ha, porosity e, and Rayleigh number Ra were presented Outputs demonstrate that the convective flow decreases with the rise of Nhs, but it enhances with the rise of Ra Porosity has opposite relationship with the temperature gradient

Turbulent superstructures in Rayleigh-Bénard convection.

Abstract: Turbulent Rayleigh-Benard convection displays a large-scale order in the form of rolls and cells on lengths larger than the layer height once the fluctuations of temperature and velocity are removed. These turbulent superstructures are reminiscent of the patterns close to the onset of convection. Here we report numerical simulations of turbulent convection in fluids at different Prandtl number ranging from 0.005 to 70 and for Rayleigh numbers up to 107. We identify characteristic scales and times that separate the fast, small-scale turbulent fluctuations from the gradually changing large-scale superstructures. The characteristic scales of the large-scale patterns, which change with Prandtl and Rayleigh number, are also correlated with the boundary layer dynamics, and in particular the clustering of thermal plumes at the top and bottom plates. Our analysis suggests a scale separation and thus the existence of a simplified description of the turbulent superstructures in geo- and astrophysical settings.

Double-Diffusive Convection at Low Prandtl Number

Abstract: This work reviews present knowledge of double-diffusive convection at low Prandtl number obtained using direct numerical simulations, in both the fingering regime and the oscillatory regime. Particular emphasis is given to modeling the induced turbulent mixing and its impact in various astrophysical applications. The nonlinear saturation of fingering convection at low Prandtl number usually drives small-scale turbulent motions whose transport properties can be predicted reasonably accurately using a simple semi-analytical model. In some instances, large-scale internal gravity waves can be excited by a collective instability and eventually cause layering. The nonlinear saturation of oscillatory double-diffusive convection exhibits much more complex behavior. Weakly stratified systems always spontaneously transition into layered convection associated with very efficient mixing. More strongly stratified systems remain dominated by weak wave turbulence unless they are initialized into a layered state. The eff...

Moist Orographic Convection: Physical Mechanisms and Links to Surface-Exchange Processes

Abstract: This paper reviews the current understanding of moist orographic convection and its regulation by surface-exchange processes. Such convection tends to develop when and where moist instability coincides with sufficient terrain-induced ascent to locally overcome convective inhibition. The terrain-induced ascent can be owing to mechanical (airflow over or around an obstacle) and/or thermal (differential heating over sloping terrain) forcing. For the former, the location of convective initiation depends on the dynamical flow regime. In “unblocked” flows that ascend the barrier, the convection tends to initiate over the windward slopes, while in “blocked” flows that detour around the barrier, the convection tends to initiate upstream and/or downstream of the high terrain where impinging flows split and rejoin, respectively. Processes that destabilize the upstream flow for mechanically forced moist convection include large-scale moistening and ascent, positive surface sensible and latent heat fluxes, and differential advection in baroclinic zones. For thermally forced flows, convective initiation is driven by thermally direct circulations with sharp updrafts over or downwind of the mountain crest (daytime) or foot (nighttime). Along with the larger-scale background flow, local evapotranspiration and transport of moisture, as well as thermodynamic heterogeneities over the complex terrain, regulate moist instability in such events. Longstanding limitations in the quantitative understanding of related processes, including both convective preconditioning and initiation, must be overcome to improve the prediction of this convection, and its collective effects, in weather and climate models.

Rheological and heat transfer effects in fused filament fabrication

Abstract: The fused filament fabrication (FFF) process is similar to classic extrusion operations; solid polymer is melted, pressurized, and extruded to produce an object. At this level of investigation, it appears no new science or engineering is required. However, FFF has heat transfer limitations that are unique to it, due to its small throughput, not encountered in contemporary polymer processing, negating the use of present-day correlations or heuristics. Here, we quantify heat transfer by rheological modeling of the pressure drop data in the process to generate a general Nusselt number–Graetz number correlation. This is the first time the pressure has been measured in the die (nozzle) during normal printing that we accomplished by monitoring the power used to drive the hot end. Ultimately, we find that fouling within the region used to melt/soften the polymer significantly reduces the heat transfer rate.

Turbulent superstructures in Rayleigh-B\'enard convection

Abstract: Turbulent Rayleigh-Benard convection displays a large-scale order in the form of rolls and cells on lengths larger than the layer height once the fluctuations of temperature and velocity are removed. These turbulent superstructures are reminiscent of the patterns close to the onset of convection. They are analyzed by numerical simulations of turbulent convection in fluids at different Prandtl number ranging from 0.005 to 70 and for Rayleigh numbers up to $10^7$. For each case, we identify characteristic scales and times that separate the fast, small-scale turbulent fluctuations from the gradually changing large-scale superstructures. The characteristic scales of the large-scale patterns, which change with Prandtl and Rayleigh number, are also found to be correlated with the boundary layer dynamics, and in particular the clustering of thermal plumes at the top and bottom plates. Our analysis suggests a scale separation and thus the existence of a simplified description of the turbulent superstructures in geo- and astrophysical settings.

Effects of partial slip on entropy generation and MHD combined convection in a lid-driven porous enclosure saturated with a Cu–water nanofluid

Abstract: In this work, the influences of heat generation/absorption and nanofluid volume fraction on the entropy generation and MHD combined convection heat transfer in a porous enclosure filled with a Cu–water nanofluid are studied numerically with of partial slip effect. The finite volume technique is utilized to solve the dimensionless equations governing the problem. A comparison with already published studies is conducted, and the data are found to be in an excellent agreement. The minimization of entropy generation and the local heat transfer according to various values of the controlling parameters are reported in detail. The outcome indicates that an augmentation in the heat generation/absorption parameter decreases the Nusselt number. Also, when the volume fraction is raised, the Nusselt number and entropy generation are reduced. The impact of Hartmann number on heat transfer and the Richardson number on the entropy generation and the thermal rendering criteria are also presented and discussed.

Residual stress development in selective laser-melted Ti6Al4V: a parametric thermal modelling approach

Abstract: High cooling rates within the selective laser melting (SLM) process can generate large residual stresses within fabricated components. Understanding residual stress development in the process and devising methods for in-situ reduction continues to be a challenge for industrial users of this technology. Computationally efficient FEA models representative of the process dynamics (temperature evolution and associated solidification behaviour) are necessary for understanding the effect of SLM process parameters on the underlying phenomenon of residual stress build-up. The objective of this work is to present a new modelling approach to simulate the temperature distribution during SLM of Ti6Al4V, as well as the resulting melt-pool size, solidification process, associated cooling rates and temperature gradients leading to the residual stress build-up. This work details an isotropic enhanced thermal conductivity model with the SLM laser modelled as a penetrating volumetric heat source. An enhanced laser penetration approach is used to account for heat transfer in the melt-pool due to Marangoni convection. Results show that the developed model was capable of predicting the temperature distribution in the laser/powder interaction zone, solidification behaviour, the associated cooling rates, melt-pool width (with 14.5% error) and melt-pool depth (with 3% error) for SLM Ti6Al4V. The model was capable of predicting the differential solidification behaviour responsible for residual stress build-up in SLM components. The model-predicted trends in cooling rates and temperature gradients for varying SLM parameters correlated with experimentally measured residual stress trends. Thus, the model was capable of accurately predicting the trends in residual stress for varying SLM parameters. This is the first work based on the enhanced penetrating volumetric heat source, combined with an isotropic enhanced thermal conductivity approach. The developed model was validated by comparing FEA melt-pool dimensions with experimental melt-pool dimensions. Secondly, the model was validated by comparing the temperature evolution along the laser scan path with experimentally measured temperatures from published literature.

How surface roughness reduces heat transport for small roughness heights in turbulent Rayleigh-Bénard convection

Abstract: Rough surfaces have been widely used as an efficient way to enhance the heat-transfer efficiency in turbulent thermal convection. In this paper, however, we show that roughness does not always mean a heat-transfer enhancement, but in some cases it can also reduce the overall heat transport through the system. To reveal this, we carry out numerical investigations of turbulent Rayleigh–Benard convection over rough conducting plates. Our study includes two-dimensional (2D) simulations over the Rayleigh number range and three-dimensional (3D) simulations at . The Prandtl number is fixed to for both the 2D and the 3D cases. At a fixed Rayleigh number , reduction of the Nusselt number is observed for small roughness height , whereas heat-transport enhancement occurs for large . The crossover between the two regimes yields a critical roughness height , which is found to decrease with increasing as . Through dimensional analysis, we provide a physical explanation for this dependence. The physical reason for the reduction is that the hot/cold fluid is trapped and accumulated inside the cavity regions between the rough elements, leading to a much thicker thermal boundary layer and thus impeding the overall heat flux through the system.