Middle Holocene palaeoflood extremes of the Lower Rhine

2012 ◽  
Vol 44 (2) ◽  
pp. 248-263 ◽  
Author(s):  
Willem H. J. Toonen ◽  
Michiel M. de Molenaar ◽  
Frans P. M. Bunnik ◽  
Hans Middelkoop
Keyword(s):  
Sensitivity Analysis ◽  
Large Range ◽  
Hydraulic Model ◽  
Flood Frequency ◽  
Minimum Size ◽  
Extreme Floods ◽  
Middle Holocene ◽  
Input Parameters ◽  
Rhine Valley ◽  
Lower Rhine

A Chézy-based hydraulic model was run to estimate the magnitude of extreme floods of Middle Holocene age in the Lower Rhine Valley (Germany). Input parameters were gathered from the field and literature, and used in ten scenarios to calculate a best guess estimate for the minimum size of extreme floods. These events have been registered as slackwater deposits on elevated terrace levels and in a palaeochannel fill. The modelled minimum discharge is 13,250 m3 sec−1 for a Middle Holocene flood with an estimated recurrence interval between 1,250 and 2,500 years. A sensitivity analysis on different input parameters enables evaluation of factors which cause the relatively large range in modelled discharges. Understanding the origin of uncertainties in modelled discharges is important for making geologically based calculations of palaeoflood magnitudes important in modern flood frequency analyses, which generally lack information on the magnitudes of rare events.

scholarly journals Sensitivity analysis and parameter selection for detecting aggregations in acoustic data

2006 ◽  
Vol 64 (1) ◽  
pp. 160-168 ◽  
Author(s):  
Julian M. Burgos ◽  
John K. Horne
Keyword(s):  
Sensitivity Analysis ◽  
Latin Hypercube Sampling ◽  
Detection Algorithm ◽  
Design Sensitivity ◽  
Walleye Pollock ◽  
Parameter Selection ◽  
Minimum Size ◽  
Acoustic Data ◽  
Input Parameters ◽  
Selection For

Abstract Burgos, J. M., and Horne, J. K. 2007. Sensitivity analysis and parameter selection for detecting aggregations in acoustic data. ICES Journal of Marine Science, 64: 160–168. A global sensitivity analysis was conducted on the algorithm implemented in the Echoview ® software to detect and describe aggregations in acoustic backscatter. Multiple aggregation detections were performed using walleye pollock (Theragra chalcogramma) data from the eastern Bering Sea. Walleye pollock form distinct aggregations and dense and diffuse layers. In each aggregation detection, input parameters defining minimum size, density, and distance to other aggregations were selected at random using a Latin hypercube sampling design. Sensitivity was quantified by testing for correlation among input parameters and a series of aggregation descriptors. In all, 336 correlation tests were performed, corresponding to a combination of seven detection input parameters, eight aggregation descriptors, and six transects. Among these, 181 tests were significant, indicating sensitivity between input parameters and aggregation descriptors. The aggregation-detection algorithm is sensitive to changes in threshold and minimum size, but less sensitive to changes in the connectivity criterion among aggregations.

Sobol′ Sensitivity Analysis Using a Neural Network Model of a LB-LOCA in the ZION Nuclear Power Plant With CATHARE-2 V2.5 Code

2013 ◽  
Author(s):  
Fabrice Fouet ◽  
Pierre Probst
Keyword(s):  
Neural Network ◽  
Sensitivity Analysis ◽  
Nuclear Power ◽  
Uncertainty Propagation ◽  
Correlation Coefficients ◽  
Computer Code ◽  
Numerical Application ◽  
Loss Of Coolant Accident ◽  
Input Parameters ◽  
Artificial Neural Network Ann

In nuclear safety, the Best-Estimate (BE) codes may be used in safety demonstration and licensing, provided that uncertainties are added to the relevant output parameters before comparing them with the acceptance criteria. The uncertainty of output parameters, which comes mainly from the lack of knowledge of the input parameters, is evaluated by estimating the 95% percentile with a high degree of confidence. IRSN, technical support of the French Safety Authority, developed a method of uncertainty propagation. This method has been tested with the BE code used is CATHARE-2 V2.5 in order to evaluate the Peak Cladding Temperature (PCT) of the fuel during a Large Break Loss Of Coolant Accident (LB-LOCA) event, starting from a large number of input parameters. A sensitivity analysis is needed in order to limit the number of input parameters and to quantify the influence of each one on the response variability of the numerical model. Generally, the Global Sensitivity Analysis (GSA) is done with linear correlation coefficients. This paper presents a new approach to perform a more accurate GSA to determine and to classify the main uncertain parameters: the Sobol′ methodology. The GSA requires simulating many sets of parameters to propagate uncertainties correctly, which makes of it a time-consuming approach. Therefore, it is natural to replace the complex computer code by an approximate mathematical model, called response surface or surrogate model. We have tested Artificial Neural Network (ANN) methodology for its construction and the Sobol′ methodology for the GSA. The paper presents a numerical application of the previously described methodology on the ZION reactor, a Westinghouse 4-loop PWR, which has been retained for the BEMUSE international problem [8]. The output is the first maximum PCT of the fuel which depends on 54 input parameters. This application outlined that the methodology could be applied to high-dimensional complex problems.

scholarly journals Meeting the growth challenge: developing the next level organization for GASA Group Germany

2018 ◽  
Vol 21 (5) ◽  
pp. 641-654
Author(s):  
Paul Philipp Reifferscheidt ◽  
Dietrich Darr
Keyword(s):  
Value Chain ◽  
Rapid Growth ◽  
Business Organizations ◽  
Organizational Model ◽  
Changing Environment ◽  
Potted Plants ◽  
Intangible Resources ◽  
Rhine Valley ◽  
Successful Business ◽  
Lower Rhine

In order to remain successful, business organizations need to continuously adapt and respond to a changing environment. Rapid growth poses significant challenges to managers, not least with regard to maintaining the balance between efficiency and creativity in their organizations. Using the example of a wholesale company operating in the potted plants value chain in the lower Rhine valley, Germany, the case illustrates how the company was able to exploit the opportunities arising from the concentration in the value chain, and the necessity to adjust their organizational model in response to these changes. The case chooses the example of a small- and medium-sized enterprise (SME) as such firms constitute the prevalent type of enterprises in Germany. Simultaneously, SMEs often find it particularly difficult to adapt their tangible and intangible resources to such changes. The current material is intended to help train future managers mastering this challenge.

Seismic hazard sensitivity analysis: A multi-parameter approach

1991 ◽  
Vol 81 (3) ◽  
pp. 796-817
Author(s):  
Nitzan Rabinowitz ◽  
David M. Steinberg
Keyword(s):  
Sensitivity Analysis ◽  
Seismic Hazard ◽  
Subjective Probability ◽  
Probability Distributions ◽  
Input Parameter ◽  
The Other ◽  
Probabilistic Assessment ◽  
Input Parameters ◽  
Parameter Approach

Abstract We propose a novel multi-parameter approach for conducting seismic hazard sensitivity analysis. This approach allows one to assess the importance of each input parameter at a variety of settings of the other input parameters and thus provides a much richer picture than standard analyses, which assess each input parameter only at the default settings of the other parameters. We illustrate our method with a sensitivity analysis of seismic hazard for Jerusalem. In this example, we find several input parameters whose importance depends critically on the settings of other input parameters. This phenomenon, which cannot be detected by a standard sensitivity analysis, is easily diagnosed by our method. The multi-parameter approach can also be used in the context of a probabilistic assessment of seismic hazard that incorporates subjective probability distributions for the input parameters.

Validation and Parametric Investigations of an Internal Permanent Magnet Motor Using a Lumped Parameter Thermal Model

2021 ◽  
Author(s):  
Sebastien Sequeira ◽  
Kevin Bennion ◽  
J. Emily Cousineau ◽  
Sreekant Narumanchi ◽  
Gilberto Moreno ◽  
...  
Keyword(s):  
Experimental Data ◽  
Sensitivity Analysis ◽  
Permanent Magnet ◽  
Power Density ◽  
Heat Removal ◽  
Maximum Difference ◽  
Modeling Framework ◽  
Element Analysis ◽  
Lumped Parameter ◽  
Input Parameters

Abstract One of the key challenges for the electric vehicle industry is to develop high-power-density electric motors. Achieving higher power density requires efficient heat removal from inside the motor. In order to improve thermal management, a multi-physics modeling framework that is able to accurately predict the behavior of the motor, while being computationally efficient, is essential. This paper first presents a detailed validation of a Lumped Parameter Thermal Network (LPTN) model of an Internal Permanent Magnet synchronous motor within the commercially available Motor-CAD® modeling environment. The validation is based on temperature comparison with experimental data and with more detailed Finite Element Analysis (FEA). All critical input parameters of the LPTN are considered in detail for each layer of the stator, especially the contact resistances between the impregnation, liner, laminations and housing. Finally, a sensitivity analysis for each of the critical input parameters is provided. A maximum difference of 4% - for the highest temperature in the slot-winding and the end-winding - was found between the LPTN and the experimental data. Comparing the results from the LPTN and the FEA model, the maximum difference was 2% for the highest temperature in the slot-winding and end-winding. As for the LTPN sensitivity analysis, the thermal parameter with the highest sensitivity was found to be the liner-to-lamination contact resistance.

scholarly journals Climatic and basin factors affecting the flood frequency curve: PART II – A full sensitivity analysis based on the continuous simulation approach combined with a factorial experimental design

2000 ◽  
Vol 4 (3) ◽  
pp. 483-498 ◽  
Author(s):  
M. Franchini ◽  
A. M. Hashemi ◽  
P. E. O’Connell
Keyword(s):  
Sensitivity Analysis ◽  
Soil Moisture ◽  
Experimental Design ◽  
Probability Distribution ◽  
Flood Frequency ◽  
Rainfall Regime ◽  
Model Parameters ◽  
Rainfall Runoff ◽  
Factorial Experimental Design ◽  
Frequency Curve

Abstract. The sensitivity analysis described in Hashemi et al. (2000) is based on one-at-a-time perturbations to the model parameters. This type of analysis cannot highlight the presence of parameter interactions which might indeed affect the characteristics of the flood frequency curve (ffc) even more than the individual parameters. For this reason, the effects of the parameters of the rainfall, rainfall runoff models and of the potential evapotranspiration demand on the ffc are investigated here through an analysis of the results obtained from a factorial experimental design, where all the parameters are allowed to vary simultaneously. This latter, more complex, analysis confirms the results obtained in Hashemi et al. (2000) thus making the conclusions drawn there of wider validity and not related strictly to the reference set selected. However, it is shown that two-factor interactions are present not only between different pairs of parameters of an individual model, but also between pairs of parameters of different models, such as rainfall and rainfall-runoff models, thus demonstrating the complex interaction between climate and basin characteristics affecting the ffc and in particular its curvature. Furthermore, the wider range of climatic regime behaviour produced within the factorial experimental design shows that the probability distribution of soil moisture content at the storm arrival time is no longer sufficient to explain the link between the perturbations to the parameters and their effects on the ffc, as was suggested in Hashemi et al. (2000). Other factors have to be considered, such as the probability distribution of the soil moisture capacity, and the rainfall regime, expressed through the annual maximum rainfalls over different durations. Keywords: Monte Carlo simulation; factorial experimental design; analysis of variance (ANOVA)

Holocene extreme paleofloods and their climatological context, Upper Colorado River Basin, USA

2020 ◽  
Vol 44 (5) ◽  
pp. 727-745
Author(s):  
Tao Liu ◽  
Lin Ji ◽  
Victor R Baker ◽  
Tessa M Harden ◽  
Michael L Cline
Keyword(s):  
River Basin ◽  
Colorado River ◽  
Meta Analysis ◽  
Flood Frequency ◽  
Colorado River Basin ◽  
Extreme Floods ◽  
Upper Colorado River Basin ◽  
Extreme Flood ◽  
Southwestern Usa ◽  
Extreme Flooding

Given its singular importance for water resources in the southwestern USA, the Upper Colorado River Basin (UCRB) is remarkable for the paucity of its conventional hydrological record of extreme flooding. Short-term record-based flood frequency analyses lead to very great aleatory uncertainties about infrequent extreme flood events and their climate-driven causal associations. This study uses paleoflood hydrology to examine a small portion of the underutilized, but very extensive natural record of Holocene extreme floods in the UCRB. We perform a meta-analysis of 82 extreme paleofloods from 18 slack water deposit sites in the UCRB to show linkages between Holocene climate patterns and extreme floods. The analysis demonstrates several clusters of extreme flood activity: 8040–7960, 4400–4300, 3600–3460, 2900–2740, 2390–1980, 1810–720, and 600–0 years BP. The extreme paleofloods were found to occur during both dry and wet periods in the paleoclimate record. When compared with independent paleoclimatic records across the Rocky Mountains and the southwestern USA, the observed temporal clustering pattern of UCRB extreme paleofloods shows associations with periods of abruptly intensified North Pacific-derived storms connected with enhanced variability of El Niño. This approach demonstrates the value of creating paleohydrological databases and comparing them with hydro-climatic proxies in order to identify natural patterns and to discover possible linkages to fundamental processes such as changes in climate.

scholarly journals Application of sensitivity analysis and genopt to optimize the energy performance of a building in Morocco

2018 ◽  
Vol 7 (4) ◽  
pp. 2068 ◽  
Author(s):  
Abdelhadi Serbouti ◽  
Mourad Rattal ◽  
Abdellah Boulal ◽  
Mohammed Harmouchi ◽  
Azeddine Mouhsen
Keyword(s):  
Sensitivity Analysis ◽  
Residential Buildings ◽  
Energy Performance ◽  
High Energy ◽  
Performance Requirement ◽  
Input Parameters ◽  
Sensitivity Analysis Method ◽  
Python Programming ◽  
Industry Sectors ◽  
High Energy Consumption

The worldwide demographic and economic growth increases the global need for energy and directly contributes to climate change. In Morocco, the residential real estate is the third largest consumer of energy after transport and industry sectors. Thus, the aim of this study is to help engineers improve the energy performance of residential buildings by coupling the TRNSYS software both with a sensitivity analysis method and with an optimization tool. In fact, sensitivity analysis allows reducing the number of input parameters of any studied model, by ranking their degree of impact on any chosen output, and then discard the parameters with the least influence on that output. To do so, we developed algorithms in Python programming language to combine the open source library SALib, available in Github platform, with the TRNSYS software. Then, the chosen input parameters can be optimized through coupling the generic optimization program Genopt with TRNSYS. This article will also explain how these tools were applied to reduce the heating & air-conditioning needs of a high-energy consumption building in Morocco, while studying the variation of nineteen input parameters in TRNSYS. The main aim is to meet the energy performance requirement of the Moroccan thermal regulation for buildings.  

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