scholarly journals Sensitivity Analysis of Unsteady Flow Fields and Impact of Measurement Strategy

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Takashi Misaka ◽  
Shigeru Obayashi
Keyword(s):  
Sensitivity Analysis ◽  
Data Assimilation ◽  
Three Dimensional ◽  
Measurement Data ◽  
Flow Fields ◽  
Vortical Flow ◽  
Measurement Strategy ◽  
The Difference ◽  
Flow Variables ◽  
The Impact

Difficulty of data assimilation arises from a large difference between the sizes of a state vector to be determined, that is, the number of spatiotemporal mesh points of a discretized numerical model and a measurement vector, that is, the amount of measurement data. Flow variables on a large number of mesh points are hardly defined by spatiotemporally limited measurements, which poses an underdetermined problem. In this study we conduct the sensitivity analysis of two- and three-dimensional vortical flow fields within a framework of data assimilation. The impact of measurement strategy, which is evaluated by the sensitivity of the 4D-Var cost function with respect to measurements, is investigated to effectively determine a flow field by limited measurements. The assimilation experiment shows that the error defined by the difference between the reference and assimilated flow fields is reduced by using the sensitivity information to locate the limited number of measurement points. To conduct data assimilation for a long time period, the 4D-Var data assimilation and the sensitivity analysis are repeated with a short assimilation window.

scholarly journals Sensitivity analysis of Economic Value Added as a dominant indicator of concept of economic profit

2021 ◽  
Vol 129 ◽  
pp. 03014
Author(s):  
Dusan Karpac ◽  
Viera Bartosova
Keyword(s):  
Sensitivity Analysis ◽  
Economic Value ◽  
Value Added ◽  
Economic Value Added ◽  
Financial Status ◽  
Economic Profit ◽  
Methods Of Synthesis ◽  
The Difference ◽  
Individual Variables ◽  
The Impact

Research background: The modern goal of enterprises, value creation, is achieved through the concept of economic profit. Profit, as part of profit or loss, is one of the most important flows, pointing to how efficiently corporate capital is used in an entity (Coatney & Poliak, 2020). The article deals with the difference between accounting and economic profit, the selected form of economic profit - the EVA indicator. The economic value added (EVA) indicator is one of the best-known modern indicators of a company's performance (Siekelova et al., 2019). It shows whether the given entity increases its value or only earns for its economic survival. The benefit of this indicator is the valuation of equity and taking into account the risk. It is difficult to express the economic profit itself, therefore the article also addresses the issue of its calculation (Shah et al., 2016). The company needs to know its financial status and the direction it is heading, so we decided to calculate a selected form of economic profit. Purpose of the article: The company needs to know its financial status and the direction it is heading, so we decided to calculate a selected form of economic profit. When expressing the value of the economic value added indicator, it is also important to know the items and components of the calculation that have the strongest meaning and effect on the possible amount of the indicator. Given this, we decided to use a sensitivity analysis, which points to the effect of individual variables that participate in the construction of the EVA calculation. Methods: In this work, the methods of induction, deduction, and comparison were used to obtain a true picture of the subject issue. Methods of synthesis and analysis of the researched issues were also used. Findings & Value added: In the paper there is pointed out the intensity of the impact of individual variables that entered into the calculation of the economic value added indicator as a dominant indicator of concept of economic profit.

scholarly journals Impact of Urban Undersea Tunnel Longitudinal Slope on the Visual Characteristics of Drivers

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Shoushuo Wang ◽  
Zhigang Du ◽  
Fangtong Jiao ◽  
Libo Yang ◽  
Yudan Ni
Keyword(s):  
Repeated Measures ◽  
Three Dimensional ◽  
Fixation Time ◽  
Eye Tracker ◽  
Three Dimensional Model ◽  
Longitudinal Slope ◽  
Visual Characteristics ◽  
The Difference ◽  
Vehicle Test ◽  
The Impact

This study aims to investigate the impact of the urban undersea tunnel longitudinal slope on the visual characteristics of drivers. 20 drivers were enrolled to conduct the real vehicle test of the urban undersea tunnel. First, the data of average fixation time and visual lobe were collected by an eye tracker. The differential significance was tested using the one-way repeated measures analysis of variance (ANOVA). Then, the difference between the up-and-down slope (direction) factor and the longitudinal slope (percent) factor on the two indexes were analyzed using the two-way repeated measures ANOVA. Second, by constructing a Lorentz model, the impact of the longitudinal slope on the average fixation time and the visual lobe were analyzed. Besides, a three-dimensional model of the longitudinal slope, average fixation time, and visual lobe was quantified. The results showed that the average fixation time and visual lobe under different longitudinal slopes markedly differed when driving on the uphill and downhill sections. The average fixation time and visual lobe under two factors were markedly different. Moreover, with an increase in the longitudinal slope, the average fixation time exhibited a trend of increasing first then decreasing; the visual lobe exhibited a trend of decreasing first and then increasing. The average fixation time reached the minimum and maximum value when the slope was 2.15% and 4.0%, whereas the visual lobe reached the maximum and minimum value when the slope was 2.88% and 4.0%. Overall, the longitudinal slope exerted a great impact on the visual load of the driver.

Sampling Error Correction Evaluated Using a Convective-Scale 1000-Member Ensemble

2019 ◽  
Vol 148 (3) ◽  
pp. 1229-1249 ◽  
Author(s):  
Tobias Necker ◽  
Martin Weissmann ◽  
Yvonne Ruckstuhl ◽  
Jeffrey Anderson ◽  
Takemasa Miyoshi
Keyword(s):  
Sensitivity Analysis ◽  
Data Assimilation ◽  
Error Correction ◽  
Degrees Of Freedom ◽  
Sampling Error ◽  
Lookup Table ◽  
Ensemble Prediction ◽  
Sampling Errors ◽  
Convective Scale ◽  
The Impact

Abstract State-of-the-art ensemble prediction systems usually provide ensembles with only 20–250 members for estimating the uncertainty of the forecast and its spatial and spatiotemporal covariance. Given that the degrees of freedom of atmospheric models are several magnitudes higher, the estimates are therefore substantially affected by sampling errors. For error covariances, spurious correlations lead to random sampling errors, but also a systematic overestimation of the correlation. A common approach to mitigate the impact of sampling errors for data assimilation is to localize correlations. However, this is a challenging task given that physical correlations in the atmosphere can extend over long distances. Besides data assimilation, sampling errors pose an issue for the investigation of spatiotemporal correlations using ensemble sensitivity analysis. Our study evaluates a statistical approach for correcting sampling errors. The applied sampling error correction is a lookup table–based approach and therefore computationally very efficient. We show that this approach substantially improves both the estimates of spatial correlations for data assimilation as well as spatiotemporal correlations for ensemble sensitivity analysis. The evaluation is performed using the first convective-scale 1000-member ensemble simulation for central Europe. Correlations of the 1000-member ensemble forecast serve as truth to assess the performance of the sampling error correction for smaller subsets of the full ensemble. The sampling error correction strongly reduced both random and systematic errors for all evaluated variables, ensemble sizes, and lead times.

scholarly journals Forecasting of Surface Currents via Correcting Wind Stress with Assimilation of High-Frequency Radar Data in a Three-Dimensional Model

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Lei Ren ◽  
Stephen Nash ◽  
Michael Hartnett
Keyword(s):  
Data Assimilation ◽  
High Frequency ◽  
Three Dimensional ◽  
Hf Radar ◽  
Surface Velocity ◽  
Shear Stresses ◽  
Surface Currents ◽  
Dimensional Model ◽  
Three Dimensional Model ◽  
The Impact

This paper details work in assessing the capability of a hydrodynamic model to forecast surface currents and in applying data assimilation techniques to improve model forecasts. A three-dimensional model Environment Fluid Dynamics Code (EFDC) was forced with tidal boundary data and onshore wind data, and so forth. Surface current data from a high-frequency (HF) radar system in Galway Bay were used for model intercomparisons and as a source for data assimilation. The impact of bottom roughness was also investigated. Having developed a “good” water circulation model the authors sought to improve its forecasting ability through correcting wind shear stress boundary conditions. The differences in surface velocity components between HF radar measurements and model output were calculated and used to correct surface shear stresses. Moreover, data assimilation cycle lengths were examined to extend the improvements of surface current’s patterns during forecasting period, especially for north-south velocity component. The influence of data assimilation in model forecasting was assessed using a Data Assimilation Skill Score (DASS). Positive magnitude of DASS indicated that both velocity components were considerably improved during forecasting period. Additionally, the improvements of RMSE for vector direction over domain were significant compared with the “free run.”

On the Importance of Assumptions for Bulk Flow in Hemodynamic Models of the Carotid Bifurcation

2011 ◽  
Author(s):  
Umberto Morbiducci ◽  
Diana Massai ◽  
Diego Gallo ◽  
Raffaele Ponzini ◽  
Marco A. Deriu ◽  
...  
Keyword(s):  
Vessel Wall ◽  
Carotid Bifurcation ◽  
Flow Fields ◽  
Transport Processes ◽  
Bulk Flow ◽  
Arterial System ◽  
Vortical Flow ◽  
Primary Role ◽  
4D Flow ◽  
The Impact

It is widely accepted that the local hemodynamics in the arterial system affects the atherogenic process. In particular the hemodynamic environment at the carotid artery bifurcation has been widely studied due to its predilection for atherosclerosis. Much effort has been spent in the past on image-based CFD carotid bifurcation models to assess the sensitivity to several assumptions of wall shear stress (WSS)-based parameters as indicators of abnormal flow. This luminal-surface-oriented approach was historically driven by histological observations on samples of the vessel wall. The consequence for this was that the reduction of the complexity of 4D flow fields focused mainly on WSS. However, few studies have provided adequate insights into the influence of these assumptions in order to confidently model the 4D hemodynamics within the bifurcation. Only recently the interest in the role played by the bulk flow in the development of the arterial disease has grown dramatically. This is the consequence of the emerging awareness that arterial hemodynamics, being an intricate process that involves interaction, reconnection and continuous re-organization of structures, could play a primary role in the regulation of mass transfer, and of its athero-protective/susceptible effect. Earlier works [1] pointed out the existence of a relationship between helical/vortical flow patterns and transport processes that could affect blood-vessel wall interaction, and might cause alterations in the residence time of atherogenic particles involved in the initiation of inflammatory response. Recently we introduced robust quantitative descriptors of bulk flow that can “reduce” the inherent complexity associated with 4D flow fields in arteries [1]. Here we present a study on the impact of assumptions on blood rheology and outflow boundary conditions (BCs) on bulk flow features within healthy carotid bifurcations, by using 4D flow descriptors. The final goal is to provide adequate insights not only to complement and to integrate, but also to extend with a quantitative characterization of the bulk flow the description currently adopted to classify altered hemodynamics.

scholarly journals Improving air quality forecasting with the assimilation of GOCI aerosol optical depth (AOD) retrievals during the KORUS-AQ period

2020 ◽  
Vol 20 (10) ◽  
pp. 6015-6036
Author(s):  
Soyoung Ha ◽  
Zhiquan Liu ◽  
Wei Sun ◽  
Yonghee Lee ◽  
Limseok Chang
Keyword(s):  
Air Quality ◽  
Data Assimilation ◽  
South Korea ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Three Dimensional ◽  
Upstream Region ◽  
Temporal And Spatial Distribution ◽  
The Impact ◽  
Air Quality Forecasting

Abstract. The Korean Geostationary Ocean Color Imager (GOCI) satellite has monitored the East Asian region in high temporal (e.g., hourly) and spatial resolution (e.g., 6 km) every day for the last decade, providing unprecedented information on air pollutants over the upstream region of the Korean Peninsula. In this study, the GOCI aerosol optical depth (AOD), retrieved at the 550 nm wavelength, is assimilated to enhance the quality of the aerosol analysis, thereby making systematic improvements to air quality forecasting over South Korea. For successful data assimilation, GOCI retrievals are carefully investigated and processed based on data characteristics such as temporal and spatial distribution. The preprocessed data are then assimilated in the three-dimensional variational data assimilation (3D-Var) technique for the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem). For the Korea–United States Air Quality (KORUS-AQ) period (May 2016), the impact of GOCI AOD on the accuracy of surface PM2.5 prediction is examined by comparing with effects of other observations including Moderate Resolution Imaging Spectroradiometer (MODIS) sensors and surface PM2.5 observations. Consistent with previous studies, the assimilation of surface PM2.5 measurements alone still underestimates surface PM2.5 concentrations in the following forecasts, and the forecast improvements last only for about 6 h. When GOCI AOD retrievals are assimilated with surface PM2.5 observations, however, the negative bias is diminished and forecast skills are improved up to 24 h, with the most significant contributions to the prediction of heavy pollution events over South Korea.

Improving air quality forecasting with the assimilation of GOCI AOD retrievals

2020 ◽  
Author(s):  
Soyoung Ha ◽  
Zhiquan Liu
Keyword(s):  
Air Quality ◽  
Data Assimilation ◽  
South Korea ◽  
Positive Impact ◽  
Fine Particulate Matter ◽  
Three Dimensional ◽  
Basis Set ◽  
Upstream Region ◽  
The Impact ◽  
Air Quality Forecasting

<p>The Korean Geostationary Ocean Color Imager (GOCI) satellite has monitored the East Asian region in high temporal and spatial resolution every day for the last decade, providing unprecedented information on air pollutants over the upstream region of the Korean peninsula. In this study, the GOCI Aerosol optical depth (AOD), retrieved at 550 nm wavelength, is assimilated to ameliorate the analysis quality, thereby making systematic improvements on air quality forecasting in South Korea. For successful data assimilation, GOCI retrievals are carefully investigated and processed based on data characteristics. The preprocessed data are then assimilated in the three-dimensional variational data assimilation (3DVAR) technique for the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem). Over the Korea-United States Air Quality (KORUS-AQ) period (May 2016), the impact of GOCI AOD on the accuracy of air quality forecasting is examined by comparing with other observations including Moderate Resolution Imaging Spectroradiometer (MODIS) sensors and fine particulate matter (PM2.5) observations at the surface. Consistent with previous studies, the assimilation of surface PM2.5 concentrations alone systematically underestimates surface PM2.5 and its positive impact lasts mainly for about 6 h. When GOCI AOD retrievals are assimilated with surface PM2.5 observations, however, the negative bias is diminished and forecasts are improved up to 24 h, with the most significant contributions to the prediction of heavy pollution events over South Korea. The talk will be finished with an introduction of our ongoing efforts on developing the assimilation capability for more sophisticated aerosol schemes such as Model for Simulating Aerosol Interactions and Chemistry (MOSAIC) and the Modal Aerosol Dynamics Model for Europe (MADE)-Volatility basis set (VBS).</p>

scholarly journals CIRA/CSU Four-Dimensional Variational Data Assimilation System

2005 ◽  
Vol 133 (4) ◽  
pp. 829-843 ◽  
Author(s):  
Milija Zupanski ◽  
Dusanka Zupanski ◽  
Tomislava Vukicevic ◽  
Kenneth Eis ◽  
Thomas Vonder Haar
Keyword(s):  
Data Assimilation ◽  
Initial Conditions ◽  
Control Variable ◽  
Three Dimensional ◽  
Atmospheric Modeling ◽  
Variational Data Assimilation ◽  
Regional Atmospheric Modeling ◽  
The Impact ◽  
Data Assimilation System ◽  
Assimilation System

A new four-dimensional variational data assimilation (4DVAR) system is developed at the Cooperative Institute for Research in the Atmosphere (CIRA)/Colorado State University (CSU). The system is also called the Regional Atmospheric Modeling Data Assimilation System (RAMDAS). In its present form, the 4DVAR system is employing the CSU/Regional Atmospheric Modeling System (RAMS) nonhydrostatic primitive equation model. The Weather Research and Forecasting (WRF) observation operator is used to access the observations, adopted from the WRF three-dimensional variational data assimilation (3DVAR) algorithm. In addition to the initial conditions adjustment, the RAMDAS includes the adjustment of model error (bias) and lateral boundary conditions through an augmented control variable definition. Also, the control variable is defined in terms of the velocity potential and streamfunction instead of the horizontal winds. The RAMDAS is developed after the National Centers for Environmental Prediction (NCEP) Eta 4DVAR system, however with added improvements addressing its use in a research environment. Preliminary results with RAMDAS are presented, focusing on the minimization performance and the impact of vertical correlations in error covariance modeling. A three-dimensional formulation of the background error correlation is introduced and evaluated. The Hessian preconditioning is revisited, and an alternate algebraic formulation is presented. The results indicate a robust minimization performance.

Numerical Investigation of the Impact of Part-Span Connectors on Aero-Thermodynamics in a Low Pressure Industrial Steam Turbine

2014 ◽  
Author(s):  
M. Häfele ◽  
J. Starzmann ◽  
M. Grübel ◽  
M. Schatz ◽  
D. M. Vogt ◽  
...  
Keyword(s):  
Steam Turbine ◽  
Numerical Study ◽  
Three Dimensional ◽  
Measurement Data ◽  
Cross Flow ◽  
Low Pressure ◽  
Wet Steam ◽  
Flow Vortex ◽  
The Impact ◽  
Non Equilibrium

A numerical study on the flow in a three stage low pressure industrial steam turbine with conical friction bolts in the last stage and lacing wires in the penultimate stage is presented and analyzed. Structured high-resolution hexahedral meshes are used for all three stages and the meshing methodology is shown for the rotor with friction bolts and blade reinforcements. Modern three-dimensional CFD with a non-equilibrium wet steam model is used to examine the aero-thermodynamic effects of the part-span connectors. A performance assessment of the coupled blades at part load, design and overload condition is presented and compared with measurement data from an industrial steam turbine test rig. Detailed flow field analyses and a comparison of blade loading between configurations with and without part-span connectors are presented in this paper. The results show significant interaction of the cross flow vortex along the part-span connector with the blade passage flow causing aerodynamic losses. This is the first time that part-span connectors are being analyzed using a non-equilibrium wet steam model. It is shown that additional wetness losses are induced by these elements.

scholarly journals A Comparison of the Impacts of Radiosonde and AMSU Radiance Observations in GSI Based 3DEnsVar and 3DVar Data Assimilation Systems for NCEP GFS

2015 ◽  
Vol 2015 ◽  
pp. 1-17 ◽  
Author(s):  
Govindan Kutty ◽  
Xuguang Wang
Keyword(s):  
Data Assimilation ◽  
Three Dimensional ◽  
Winter Season ◽  
Data Quality Control ◽  
Hybrid Data Assimilation ◽  
Environmental Prediction ◽  
Hemisphere Winter ◽  
The Impact ◽  
Data Assimilation System ◽  
Assimilation System

The impact of observations can be dependent on many factors in a data assimilation (DA) system including data quality control, preprocessing, skill of the model, and the DA algorithm. The present study focuses on comparing the impacts of observations assimilated by two different DA algorithms. A three-dimensional ensemble-variational (3DEnsVar) hybrid data assimilation system was recently developed based on the Gridpoint Statistical Interpolation (GSI) data assimilation system and was implemented operationally for the National Center for Environmental Prediction (NCEP) Global Forecast System (GFS). One question to address is, how the impacts of observations on GFS forecasts differ when assimilated by the traditional GSI-three dimensional variational (3DVar) and the new 3DEnsVar. Experiments were conducted over a 6-week period during Northern Hemisphere winter season at a reduced resolution. For both the control and data denial experiments, the forecasts produced by 3DEnsVar were more accurate than GSI3DVar experiments. The results suggested that the observations were better and more effectively exploited to increment the background forecast in 3DEnsVar. On the other hand, in GSI3DVar, where the observation will be making mostly local, isotropic increments without proper flow dependent extrapolation is more sensitive to the number and types observations assimilated.

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