scholarly journals Sensitivity of Mesoscale Model Forecasts of Icing Conditions to Land Use: Dynamic and Diabatic Impacts

2020 ◽  
Author(s):  
Erik Janzon ◽  
Heiner Körnich ◽  
Johan Arnqvist ◽  
Anna Rutgersson
Keyword(s):  
Surface Roughness ◽  
Cloud Water ◽  
Ice Accretion ◽  
Single Column ◽  
Wind Response ◽  
Primary Driver ◽  
Water Response ◽  
Cloud Ice ◽  
The Impact ◽  
Surface Vegetation

In-cloud ice mass accretion on wind turbines is a common challenge faced by energy companies operating in cold climates. On-shore wind farms in Scandinavia are often located in regions near patches of forest, the heterogeneity length scales of which are often less than the resolution of many numerical weather prediction (NWP) models. The representation of these forests--including the cloud water response to surface roughness and albedo effects related to them--must therefore be parameterized in NWP models used as meteorological input in ice prediction systems, resulting in an uncertainty that is poorly understood and to present date not quantified. The sensitivity of ice accretion forecasts to the subgrid representation of forests is examined in this study. A single column version of the HARMONIE-AROME 3D NWP model is used to determine the sensitivity of the forecast of ice accretion on wind turbines to the subgrid forest fraction. Single column simulations of a variety of icing cases at a location in northern Sweden were examined in order to investigate the impact of vegetation cover on ice accretion in varying levels of solar insulation and wind magnitudes. In mid-winter cases, the wind speed response to surface roughness was the primary driver of the vegetation effect on ice accretion. In early season cases, the cloud water response to surface albedo effects plays a secondary role in the impact of in-cloud ice accretion, with the wind response to surface roughness remaining the primary driver for the surface vegetation impact on icing. Two different surface boundary layer (SBL) forest canopy subgrid parameterizations were tested in this study that feature different methods for calculating near-surface profiles of wind, temperature, and moisture, with the ice mass accretion again following the wind response to surface vegetation between both of these schemes.

scholarly journals Single Column Model Simulations of Icing Conditions in Northern Sweden: Sensitivity to Surface Model Land Use Representation

Energies ◽  
2020 ◽  
Vol 13 (16) ◽  
pp. 4258
Author(s):  
Erik Janzon ◽  
Heiner Körnich ◽  
Johan Arnqvist ◽  
Anna Rutgersson
Keyword(s):  
Surface Roughness ◽  
Ice Accretion ◽  
Northern Sweden ◽  
Single Column ◽  
Wind Response ◽  
Primary Driver ◽  
Water Response ◽  
Cloud Ice ◽  
The Impact ◽  
Surface Vegetation

In-cloud ice mass accretion on wind turbines is a common challenge that is faced by energy companies operating in cold climates. On-shore wind farms in Scandinavia are often located in regions near patches of forest, the heterogeneity length scales of which are often less than the resolution of many numerical weather prediction (NWP) models. The representation of these forests—including the cloud water response to surface roughness and albedo effects that are related to them—must therefore be parameterized in NWP models used as meteorological input in ice prediction systems, resulting in an uncertainty that is poorly understood and, to the present date, not quantified. The sensitivity of ice accretion forecasts to the subgrid representation of forests is examined in this study. A single column version of the HARMONIE-AROME three-dimensional (3D) NWP model is used to determine the sensitivity of the forecast of ice accretion on wind turbines to the subgrid forest fraction. Single column simulations of a variety of icing cases at a location in northern Sweden were examined in order to investigate the impact of vegetation cover on ice accretion in varying levels of solar insolation and wind magnitudes. In mid-winter cases, the wind speed response to surface roughness was the primary driver of the vegetation effect on ice accretion. In autumn cases, the cloud water response to surface albedo effects plays a secondary role in the impact of in-cloud ice accretion, with the wind response to surface roughness remaining the primary driver for the surface vegetation impact on icing. Two different surface boundary layer (SBL) forest canopy subgrid parameterizations were tested in this study that feature different methods for calculating near-surface profiles of wind, temperature, and moisture, with the ice mass accretion again following the wind response to surface vegetation between both of these schemes.

scholarly journals An aqueous chemistry module for a three-dimensional cloud resolving model: Sulfate redistribution

2012 ◽  
Vol 77 (9) ◽  
pp. 1273-1285 ◽  
Author(s):  
Dragana Vujovic ◽  
Vladan Vuckovic
Keyword(s):  
Chemical Species ◽  
Chemical Conversion ◽  
Cloud Water ◽  
Ammonium Ion ◽  
Brownian Diffusion ◽  
Aqueous Chemistry ◽  
Conversion Rates ◽  
Cloud Ice ◽  
The Impact ◽  
Chemistry Module

An aqueous chemistry module is created and included into a complex 3D cloud-resolving mesoscale ARPS model to examine the characteristics of in-cloud sulfate. The complex orography of Serbia is included in the model. The chemical species included in the module are sulfur dioxide, sulfate ion, ammonium ion, hydrogen peroxide and ozone. Six water categories are considered: water vapor, cloud water, rain, cloud ice, snow and hail. Each chemical species in each microphysical category is represented by a differential equation of mass continuity. This paper gives a detailed description of the chemistry module and demonstrates the utility of an atmospheric model coupled with the chemistry module in forecasting the redistribution of chemical species in all water categories. The main mean microphysical and chemical conversion rates of sulfate averaged over a 2 h simulation period for a base run are for the oxidation of S(IV) in rain water and cloud water, SO4 2? scavenging by Brownian diffusion in cloud droplets and cloud ice as well as the impact scavenging of SO4 2? by rain. The calculated values of sulfates in all water categories and the shape of sulfate profiles depend on radar reflectivity.

scholarly journals Sensitivity Study of Ice Accretion Simulation to Roughness Thermal Correction Model

Aerospace ◽  
2021 ◽  
Vol 8 (3) ◽  
pp. 84
Author(s):  
Kevin Ignatowicz ◽  
François Morency ◽  
Héloïse Beaugendre
Keyword(s):  
Heat Transfer ◽  
Surface Roughness ◽  
Convective Heat Transfer ◽  
Convective Heat ◽  
Sensitivity Study ◽  
Correction Coefficient ◽  
Ice Accretion ◽  
Roughness Parameters ◽  
Thermal Correction ◽  
The Impact

The effects of atmospheric icing can be anticipated by Computational Fluid Dynamics (CFD). Past studies show that the convective heat transfer influences the ice accretion and is itself a function of surface roughness. Uncertainty quantification (UQ) could help quantify the impact of surface roughness parameters on the reliability of ice accretion prediction. This paper aims to quantify ice accretion uncertainties and identify the key surface roughness correction parameters contributing the most to the uncertainties in a Reynolds-Averaged Navier-Stokes (RANS) formulation. Ice accretion simulations over a rough flat plate using two thermal correction models are used to construct a RANS database. Non-Intrusive Polynomial Chaos Expansion (NIPCE) metamodels are developed to predict the convective heat transfer and icing characteristics of the RANS database. The metamodels allow for the computation of the 95% confidence intervals of the output probability distribution (PDF) and of the sensitivity indexes of the roughness parameters according to their level of influence on the outputs. For one of the thermal correction models, the most influential parameter is the roughness height, whereas for the second model it is the surface correction coefficient. In addition, the uncertainty on the freestream temperature has a minor impact on the ice accretion sensitivity compared to the uncertainty on the roughness parameters.

scholarly journals Smoothing additive manufactured parts using ns-pulsed laser radiation

2021 ◽  
Author(s):  
Florian Kuisat ◽  
Fernando Lasagni ◽  
Andrés Fabián Lasagni
Keyword(s):  
Surface Roughness ◽  
Mechanical Performance ◽  
State Of The Art ◽  
Pulsed Laser ◽  
Industrial Applications ◽  
Laser Source ◽  
Pulsed Laser Radiation ◽  
Current State ◽  
The Impact

AbstractIt is well known that the surface topography of a part can affect its mechanical performance, which is typical in additive manufacturing. In this context, we report about the surface modification of additive manufactured components made of Titanium 64 (Ti64) and Scalmalloy®, using a pulsed laser, with the aim of reducing their surface roughness. In our experiments, a nanosecond-pulsed infrared laser source with variable pulse durations between 8 and 200 ns was applied. The impact of varying a large number of parameters on the surface quality of the smoothed areas was investigated. The results demonstrated a reduction of surface roughness Sa by more than 80% for Titanium 64 and by 65% for Scalmalloy® samples. This allows to extend the applicability of additive manufactured components beyond the current state of the art and break new ground for the application in various industrial applications such as in aerospace.

scholarly journals Modeling and image quality enhancement for dynamic compressive imaging system

2021 ◽  
Vol 15 ◽  
pp. 174830262110080
Author(s):  
Changjun Zha* ◽  
Qian Zhang* ◽  
Huimin Duan
Keyword(s):  
Image Quality ◽  
Imaging System ◽  
Quality Enhancement ◽  
Target Image ◽  
Compressive Imaging ◽  
Image Quality Enhancement ◽  
System A ◽  
Single Column ◽  
The Impact

Traditional single-pixel imaging systems are aimed mainly at relatively static or slowly changing targets. When there is relative motion between the imaging system and the target, sizable deviations between the measurement values and the real values can occur and result in poor image quality of the reconstructed target. To solve this problem, a novel dynamic compressive imaging system is proposed. In this system, a single-column digital micro-mirror device is used to modulate the target image, and the compressive measurement values are obtained for each column of the image. Based on analysis of the measurement values, a new recovery model of dynamic compressive imaging is given. Differing from traditional reconstruction results, the measurement values of any column of vectors in the target image can be used to reconstruct the vectors of two adjacent columns at the same time. Contingent upon characteristics of the results, a method of image quality enhancement based on an overlapping average algorithm is proposed. Simulation experiments and analysis show that the proposed dynamic compressive imaging can effectively reconstruct the target image; and that when the moving speed of the system changes within a certain range, the system reconstructs a better original image. The system overcomes the impact of dynamically changing speeds, and affords significantly better performance than traditional compressive imaging.

Nature instigated Grey wolf algorithm for parametric optimization during machining (Milling) of polymer nanocomposites

2021 ◽  
pp. 089270572199320
Author(s):  
Prakhar Kumar Kharwar ◽  
Rajesh Kumar Verma
Keyword(s):  
Surface Roughness ◽  
Cutting Force ◽  
Polymer Nanocomposites ◽  
Feed Rate ◽  
Fitness Function ◽  
Depth Of Cut ◽  
Multiwall Carbon Nanotube ◽  
Grey Wolf ◽  
Milling Characteristics ◽  
The Impact

The new era of engineering society focuses on the utilization of the potential advantage of carbon nanomaterials. The machinability facets of nanocarbon materials are passing through an initial stage. This article emphasizes the machinability evaluation and optimization of Milling performances, namely Surface roughness (Ra), Cutting force (Fc), and Material removal rate (MRR) using a recently developed Grey wolf optimization algorithm (GWOA). The Taguchi theory-based L27 orthogonal array (OA) was employed for the Machining (Milling) of polymer nanocomposites reinforced by Multiwall carbon nanotube (MWCNT). The second-order polynomial equation was intended for the analysis of the model. These mathematical models were used as a fitness function in the GWOA to predict machining performances. The ANOVA outcomes efficiently explore the impact of machine parameters on Milling characteristics. The optimal combination for lower surface roughness value is 1.5 MWCNT wt.%, 1500 rpm of spindle speed, 50 mm/min of feed rate, and 3 mm depth of cut. For lower cutting force, 1.0 wt.%, 1500 rpm, 90 mm/min feed rate and 1 mm depth of cut and the maximize MRR was acquired at 0.5 wt.%, 500 rpm, 150 mm/min feed rate and 3 mm depth of cut. The deviation of the predicted value from the experimental value of Ra, Fc, and MRR are found as 2.5, 6.5 and 5.9%, respectively. The convergence plot of all Milling characteristics suggests the application potential of the GWO algorithm for quality improvement in a manufacturing environment.

scholarly journals A Novel Sea Surface Roughness Parameterization Based on Wave State and Sea Foam

2021 ◽  
Vol 9 (3) ◽  
pp. 246
Author(s):  
Difu Sun ◽  
Junqiang Song ◽  
Xiaoyong Li ◽  
Kaijun Ren ◽  
Hongze Leng
Keyword(s):  
Surface Roughness ◽  
Wind Speed ◽  
Drag Coefficient ◽  
Sea Surface ◽  
High Wind Speed ◽  
Wave State ◽  
Wind Speeds ◽  
Sea Surface Roughness ◽  
Extreme Wind ◽  
The Impact

A wave state related sea surface roughness parameterization scheme that takes into account the impact of sea foam is proposed in this study. Using eight observational datasets, the performances of two most widely used wave state related parameterizations are examined under various wave conditions. Based on the different performances of two wave state related parameterizations under different wave state, and by introducing the effect of sea foam, a new sea surface roughness parameterization suitable for low to extreme wind conditions is proposed. The behaviors of drag coefficient predicted by the proposed parameterization match the field and laboratory measurements well. It is shown that the drag coefficient increases with the increasing wind speed under low and moderate wind speed conditions, and then decreases with increasing wind speed, due to the effect of sea foam under high wind speed conditions. The maximum values of the drag coefficient are reached when the 10 m wind speeds are in the range of 30–35 m/s.

scholarly journals Evaluation of WRF-DART (ARW v3.9.1.1 and DART Manhattan release) multiphase cloud water path assimilation for short-term solar irradiance forecasting in a tropical environment

2019 ◽  
Vol 12 (9) ◽  
pp. 3939-3954
Author(s):  
Frederik Kurzrock ◽  
Hannah Nguyen ◽  
Jerome Sauer ◽  
Fabrice Chane Ming ◽  
Sylvain Cros ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Weather Prediction ◽  
Cloud Water ◽  
Specific Humidity ◽  
Reunion Island ◽  
Short Term ◽  
Water Path ◽  
Réunion Island ◽  
Cloud Water Path ◽  
The Impact

Abstract. Numerical weather prediction models tend to underestimate cloud presence and therefore often overestimate global horizontal irradiance (GHI). The assimilation of cloud water path (CWP) retrievals from geostationary satellites using an ensemble Kalman filter (EnKF) led to improved short-term GHI forecasts of the Weather Research and Forecasting (WRF) model in midlatitudes in case studies. An evaluation of the method under tropical conditions and a quantification of this improvement for study periods of more than a few days are still missing. This paper focuses on the assimilation of CWP retrievals in three phases (ice, supercooled, and liquid) in a 6-hourly cycling procedure and on the impact of this method on short-term forecasts of GHI for Réunion Island, a tropical island in the southwest Indian Ocean. The multilayer gridded cloud properties of NASA Langley's Satellite ClOud and Radiation Property retrieval System (SatCORPS) are assimilated using the EnKF of the Data Assimilation Research Testbed (DART) Manhattan release (revision 12002) and the advanced research WRF (ARW) v3.9.1.1. The ability of the method to improve cloud analyses and GHI forecasts is demonstrated, and a comparison using independent radiosoundings shows a reduction of specific humidity bias in the WRF analyses, especially in the low and middle troposphere. Ground-based GHI observations at 12 sites on Réunion Island are used to quantify the impact of CWP DA. Over a total of 44 d during austral summertime, when averaged over all sites, CWP data assimilation has a positive impact on GHI forecasts for all lead times between 5 and 14 h. Root mean square error and mean absolute error are reduced by 4 % and 3 %, respectively.

scholarly journals The Impact of Changes in Cloud Water pH on Aerosol Radiative Forcing

2019 ◽  
Vol 46 (7) ◽  
pp. 4039-4048 ◽  
Author(s):  
S. T. Turnock ◽  
G. W. Mann ◽  
M. T. Woodhouse ◽  
M. Dalvi ◽  
F. M. O'Connor ◽  
...  
Keyword(s):  
Radiative Forcing ◽  
Cloud Water ◽  
Aerosol Radiative Forcing ◽  
Water Ph ◽  
The Impact ◽  
Aerosol Radiative
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