Evaluation of Effects of Small-scale Heterogeneities in Dynamic Modelling of Electromagnetic Logging

KazGeo 2012 ◽  
2012 ◽  
Author(s):  
O.N. Soboleva ◽  
B.G. Mikhilenko ◽  
E.P. Kurochkina
Keyword(s):  
Dynamic Modelling ◽  
Small Scale

Dynamic Modelling of Small Scale and High Temperature ORC System Using Simulink and CoolProp

2021 ◽  
Author(s):  
Radheesh Dhanasegaran ◽  
Antti Uusitalo ◽  
Teemu Turunen-Saaresti
Keyword(s):  
High Temperature ◽  
Dynamic Modelling ◽  
Small Scale

Reconstructing floods in small-medium scale data-scarce catchments using field interview data and hydrodynamic modelling

2020 ◽  
Author(s):  
Mark Bawa Malgwi ◽  
Jorge Alberto Ramirez ◽  
Andreas Zischg ◽  
Markus Zimmermann ◽  
Stefan Schürmann ◽  
...  
Keyword(s):  
Dynamic Modelling ◽  
River Network ◽  
Interview Data ◽  
Peak Discharge ◽  
Flood Event ◽  
Small Scale ◽  
Flood Duration ◽  
Duration Data ◽  
Field Interview ◽  
Scale Data

<p>We develop a technique for reconstructing floods in small-scale data scarce regions using field interview data and hydro-dynamic modelling. The field interview data consist of flood depths and duration data collected from 300 buildings from a flood event in 2017 in Suleja/Tafa area, Nigeria. The flood event resulted from an overflow of water from five river reaches. The hydrodynamic model utilized, called CAESER LisFLOOD, is an integration of a landscape evolution model (CAESER) and a hydraulic model (LisFLOOD-FP). We employ three steps to reconstruct the 2017 Suleja/Tafa flood event. Firstly, we use a linearly increasing hydrograph to; (a) calibrate Manning’s coefficient and (b) determine optimal peak discharge on each reach. This was carried out by minimizing the Root Mean Square Error (RMSE) between the distributed observed flood depths and the simulated flood depths. Secondly, we use synthetic hydrographs with durations between 6, 12, 18, 20, 24 hours, having peak discharge (extracted from the previous step), to simulate flows on all upstream reaches. Using collected flood duration data, we minimized RMSE between distributed observed flood duration and simulated flood duration to determine optimal flow durations on each upstream reach. In the last step, utilizing peak discharge and flow duration for all upstream reaches, we carried out multiple spatial and temporal iterations to match downstream peak discharge. Thereafter, we use determined upstream hydrographs with their relative catchment response timing to simulate the entire river network. Minimum RMSE computed for the entire river network was between ±15 cm of many current studies that use distributed observed data to calibrate flood models. The method developed in this study is useful for simulating floods in regions where data such as high resolution DEMs, river bathymetry and river discharge are limited. In addition, the study extends current knowledge, on utilizing distributed flood data to determine peak discharge, from a single to multiple river networks.</p>

Nonlinear Modelling and Identification of a Small-Scale Bridge

2008 ◽  
Author(s):  
Jose´ L. Zapico ◽  
David H. Bassir ◽  
Mari´a P. Gonza´lez-Marti´nez ◽  
Marta Garci´a-Die´guez
Keyword(s):  
Experimental Data ◽  
Adaptive Sampling ◽  
Dynamic Modelling ◽  
Sampling Technique ◽  
Elastic Stiffness ◽  
Small Scale ◽  
Finite Element Models ◽  
Viscous Damping ◽  
Slip Model ◽  
Linear Elastic

The dynamic modelling and identification of a small-scale bridge is addressed in this paper. Two finite element models with linear elastic stiffness and different damping modelling has been tried. They correspond to a linear viscous damping and a nonlinear elasto-slip one. Both models were fitted to the available experimental data by a novel adaptive sampling technique that was repeated several times. In all the runs the technique yielded consistent results, which confirms its robustness. The elasto-slip model gave excellent fitting to the experimental data, while the results of the linear viscous one were poor.

Dynamic Modelling of Small Scale and High Temperature ORC System Using Simulink and CoolProp

2020 ◽  
Author(s):  
Radheesh Dhanasegaran ◽  
Antti Uusitalo ◽  
Teemu Turunen-Saaresti
Keyword(s):  
High Temperature ◽  
Dynamic Model ◽  
High Speed ◽  
Waste Heat ◽  
Fluid Pressure ◽  
Dynamic Modelling ◽  
Working Fluid ◽  
Small Scale ◽  
Condensation Process ◽  
Feed Pump

Abstract In the present work, a dynamic model has been developed for the small-scale high-temperature ORC experimental test rig at the LUT University that utilizes waste heat from a heavy-duty diesel engine exhaust. The experimental facility consists of a high-speed Turbogenerator, heat exchanger components such as recuperator, condenser, and evaporator with a pre-feed pump to boost the working fluid pressure after the condensation process constituting a cycle. The turbogenerator consists of a supersonic radial-inflow turbine, a barske type main-feed pump, and a permanent magnet type generator components connected on a single shaft. Octamethyltrisiloxane (MDM) is the chosen organic working fluid in this cycle. Matlab-Simulink environment along with the open-source thermodynamic and transport database Cool-Prop has been chosen for calculating the thermodynamic properties of the dynamic model. A functional parameter approach has been followed for modeling each block component by predefined input and output parameters, aimed at modeling the performance characteristics with a limited number of inputs for both design and off-design operations of the cycle. The dynamic model is validated with the experimental data in addition to the investigation of exhaust gas mass flow regulation that establishes a control strategy for the dynamic model.

scholarly journals Modelling and Forecasting Domestic Tourism. Case Study from Armenia

2021 ◽  
Vol 5 (2) ◽  
pp. 96-110
Author(s):  
Karen Poghosyan ◽  
Gayane Tovmasyan
Keyword(s):  
Large Scale ◽  
Dynamic Models ◽  
Dynamic Modelling ◽  
Tourism Development ◽  
Point Of View ◽  
Sufficient Evidence ◽  
Small Scale ◽  
Scale Models ◽  
Domestic Tourism ◽  
Modelling And Forecasting

This paper summarizes the arguments and counterarguments within the scientific discussion on the issue of modelling and forecasting domestic tourism. During Covid-19 many countries tried to develop domestic tourism as an alternative to inbound tourism. In Armenia domestic tourism has grown recently, and in 2020 the decrease was 33% compared to last year. The main purpose of the research is to model and forecast domestic tourism growth in Armenia. Systematization of the literary sources and approaches for solving the problem indicates that many models and different variables are used to forecast tourism development. Methodological tools of the research methods were static and dynamic models, years of research were 2001-2020, quarterly data. The paper presents the results of an empirical analysis, which showed that with the static regression analysis a 1% change in GDP will lead to a change of 4.43% in the number of domestic tourists, a 1% change in the CPI will lead to a 14.55% change in the number of domestic tourists. For dynamic modelling we used 12 competing short-term forecasting models. Based on the recursive and rolling forecast simulation results we concluded that out-of-sample forecasts obtained by the small-scale models outperform forecasts obtained by the large-scale models at all forecast horizons. So, the forecasts of the domestic tourists’ growth obtained by small-scale models are more appropriate from the practical point of view. Then, in order to check whether the differences in forecasts obtained by the different models are statistically significant we applied Diebold-Mariano test. Based on the results of this test we concluded that there is not sufficient evidence to favor large-scale over small-scale models. This means that the forecast results obtained for domestic tourist growth by using the small scale models would not be statistically different from the results obtained by the large scale models. Based on the analysis, the forecasted values for domestic tourists for the future years were determined. The results of the research can be useful for state bodies, as well as private organizations, and for everybody who wants to model and forecast tourism development.

scholarly journals Destress Blasting of Rock Mass: Multiscale Modelling and Simulation

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Paweł Baranowski ◽  
Krzysztof Damaziak ◽  
Łukasz Mazurkiewicz ◽  
Piotr Mertuszka ◽  
Witold Pytel ◽  
...  
Keyword(s):  
Rock Mass ◽  
Dynamic Modelling ◽  
Multiscale Modelling ◽  
Modelling And Simulation ◽  
Small Scale ◽  
Engineering Practice ◽  
Fe Model ◽  
Scale Problem ◽  
Destress Blasting ◽  
Local Case

In this paper, a multiscale modelling and simulation of destress blasting of rock mass is presented. The proposed and novel approach combines two separate 3D solutions: the first was obtained for the small-scale problem, face(s) blasting process, and the second for the global scale problem, seismic wave propagation within very large volumes of surrounding rock mass. Both the approaches were based on explicit dynamic modelling methodology using the central difference method. In the local case, the arbitrary Lagrangian–Eulerian (ALE) procedure with the Jones–Wilkins–Lee (JWL) equation defining an explosive material was used. For this purpose, a selected volume of a rock mass comprising a blasted mining face was modelled in detail. From the numerical simulation, pressure distribution over the modelled rock was obtained, which was the basis for an initial condition for the global 3D FE model. The peak particle velocity (ppv) distribution obtained from finite element analysis was compared with experimental outcomes. A reasonable agreement between both results was achieved; therefore, the adopted multiscale modelling approach confirmed its effectiveness and that it can be successfully implemented in further engineering practice.

Reasons to strike first

2019 ◽  
Vol 42 ◽  
Author(s):  
William Buckner ◽  
Luke Glowacki
Keyword(s):  
Intergroup Conflict ◽  
Small Scale

Abstract De Dreu and Gross predict that attackers will have more difficulty winning conflicts than defenders. As their analysis is presumed to capture the dynamics of decentralized conflict, we consider how their framework compares with ethnographic evidence from small-scale societies, as well as chimpanzee patterns of intergroup conflict. In these contexts, attackers have significantly more success in conflict than predicted by De Dreu and Gross's model. We discuss the possible reasons for this disparity.

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