Impact of sewer condition on urban flooding: an uncertainty analysis based on field observations and Monte Carlo simulations on full hydrodynamic models

2012 ◽  
Vol 65 (12) ◽  
pp. 2219-2227 ◽  
Author(s):  
M. van Bijnen ◽  
H. Korving ◽  
F. Clemens
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Surface Damage ◽  
Model Parameters ◽  
Field Observations ◽  
Sewer System ◽  
Sewer Systems ◽  
Calculation Results ◽  
Urban Pluvial Flooding ◽  
The Impact

In-sewer defects are directly responsible for affecting the performance of sewer systems. Notwithstanding the impact of the condition of the assets on serviceability, sewer performance is usually assessed assuming the absence of in-sewer defects. This leads to an overestimation of serviceability. This paper presents the results of a study in two research catchments on the impact of in-sewer defects on urban pluvial flooding at network level. Impacts are assessed using Monte Carlo simulations with a full hydrodynamic model of the sewer system. The studied defects include root intrusion, surface damage, attached and settled deposits, and sedimentation. These defects are based on field observations and translated to two model parameters (roughness and sedimentation). The calculation results demonstrate that the return period of flooding, number of flooded locations and flooded volumes are substantially affected by in-sewer defects. Irrespective of the type of sewer system, the impact of sedimentation is much larger than the impact of roughness. Further research will focus on comparing calculated and measured behaviour in one of the research catchments.

scholarly journals A new sewage exfiltration model – parameters and calibration

2011 ◽  
Vol 63 (10) ◽  
pp. 2294-2299 ◽  
Author(s):  
Christian Karpf ◽  
Peter Krebs
Keyword(s):  
Waste Water ◽  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Experimental Studies ◽  
Model Parameters ◽  
Potential Danger ◽  
Detailed Consideration ◽  
Sewer Systems ◽  
The Law ◽  
Groundwater Infiltration

Exfiltration of waste water from sewer systems represents a potential danger for the soil and the aquifer. Common models, which are used to describe the exfiltration process, are based on the law of Darcy, extended by a more or less detailed consideration of the expansion of leaks, the characteristics of the soil and the colmation layer. But, due to the complexity of the exfiltration process, the calibration of these models includes a significant uncertainty. In this paper, a new exfiltration approach is introduced, which implements the dynamics of the clogging process and the structural conditions near sewer leaks. The calibration is realised according to experimental studies and analysis of groundwater infiltration to sewers. Furthermore, exfiltration rates and the sensitivity of the approach are estimated and evaluated, respectively, by Monte-Carlo simulations.

scholarly journals The effect of ionic defect interactions on the hydration of yttrium-doped barium zirconate

2021 ◽  
Author(s):  
Sebastian Eisele ◽  
Fabian M. Draber ◽  
Steffen Grieshammer
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
First Principles ◽  
Barium Zirconate ◽  
First Principles Calculations ◽  
Defect Interactions ◽  
The Impact ◽  
Ionic Defect

First principles calculations and Monte Carlo simulations reveal the impact of defect interactions on the hydration of barium-zirconate.

scholarly journals A Monte Carlo Determination of Dose and Range Uncertainties for Preclinical Studies with a Proton Beam

Cancers ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 1889
Author(s):  
Arthur Bongrand ◽  
Charbel Koumeir ◽  
Daphnée Villoing ◽  
Arnaud Guertin ◽  
Ferid Haddad ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Proton Beam ◽  
Dose Response ◽  
Small Animal ◽  
Absorbed Dose ◽  
Normal Tissue Damage ◽  
Dose Homogeneity ◽  
And Control ◽  
The Impact

Proton therapy (PRT) is an irradiation technique that aims at limiting normal tissue damage while maintaining the tumor response. To study its specificities, the ARRONAX cyclotron is currently developing a preclinical structure compatible with biological experiments. A prerequisite is to identify and control uncertainties on the ARRONAX beamline, which can lead to significant biases in the observed biological results and dose–response relationships, as for any facility. This paper summarizes and quantifies the impact of uncertainty on proton range, absorbed dose, and dose homogeneity in a preclinical context of cell or small animal irradiation on the Bragg curve, using Monte Carlo simulations. All possible sources of uncertainty were investigated and discussed independently. Those with a significant impact were identified, and protocols were established to reduce their consequences. Overall, the uncertainties evaluated were similar to those from clinical practice and are considered compatible with the performance of radiobiological experiments, as well as the study of dose–response relationships on this proton beam. Another conclusion of this study is that Monte Carlo simulations can be used to help build preclinical lines in other setups.

The electron transport that occurs within wurtzite zinc oxide and the application of stress

MRS Advances ◽  
2017 ◽  
Vol 2 (48) ◽  
pp. 2627-2632 ◽  
Author(s):  
Poppy Siddiqua ◽  
Michael S. Shur ◽  
Stephen K. O’Leary
Keyword(s):  
Monte Carlo ◽  
Zinc Oxide ◽  
Electron Transport ◽  
Velocity Field ◽  
Monte Carlo Simulations ◽  
Significant Role ◽  
Energy Gap ◽  
Device Application ◽  
The Impact

ABSTRACTWe examine how stress has the potential to shape the character of the electron transport that occurs within ZnO. In order to narrow the scope of this analysis, we focus on a determination of the velocity-field characteristics associated with bulk wurtzite ZnO. Monte Carlo simulations of the electron transport are pursued for the purposes of this analysis. Rather than focusing on the impact of stress in of itself, instead we focus on the changes that occur to the energy gap through the application of stress, i.e., energy gap variations provide a proxy for the amount of stress. Our results demonstrate that stress plays a significant role in shaping the form of the velocity-field characteristics associated with ZnO. This dependence could potentially be exploited for device application purposes.

scholarly journals Modelling the spread of American foulbrood in honeybees

2013 ◽  
Vol 10 (88) ◽  
pp. 20130650 ◽  
Author(s):  
Samik Datta ◽  
James C. Bull ◽  
Giles E. Budge ◽  
Matt J. Keeling
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Control Strategies ◽  
Sir Model ◽  
Model Parameters ◽  
American Foulbrood ◽  
Inspection Period ◽  
Stochastic Sir ◽  
Stochastic Sir Model ◽  
The Impact

We investigate the spread of American foulbrood (AFB), a disease caused by the bacterium Paenibacillus larvae , that affects bees and can be extremely damaging to beehives. Our dataset comes from an inspection period carried out during an AFB epidemic of honeybee colonies on the island of Jersey during the summer of 2010. The data include the number of hives of honeybees, location and owner of honeybee apiaries across the island. We use a spatial SIR model with an underlying owner network to simulate the epidemic and characterize the epidemic using a Markov chain Monte Carlo (MCMC) scheme to determine model parameters and infection times (including undetected ‘occult’ infections). Likely methods of infection spread can be inferred from the analysis, with both distance- and owner-based transmissions being found to contribute to the spread of AFB. The results of the MCMC are corroborated by simulating the epidemic using a stochastic SIR model, resulting in aggregate levels of infection that are comparable to the data. We use this stochastic SIR model to simulate the impact of different control strategies on controlling the epidemic. It is found that earlier inspections result in smaller epidemics and a higher likelihood of AFB extinction.

A Preliminary Assessment of the Robustness of Signal Detection Theory Estimates Using Monte Carlo Simulations for Human Factors Professionals

2018 ◽  
Vol 62 (1) ◽  
pp. 1301-1305
Author(s):  
Brittany Neilson ◽  
Dmitrii Paniukov ◽  
Martina I. Klein
Keyword(s):  
Monte Carlo ◽  
Human Factors ◽  
Monte Carlo Simulations ◽  
Signal Detection ◽  
Signal Detection Theory ◽  
Detection Theory ◽  
Preliminary Assessment ◽  
Assumption Violations ◽  
Homogeneity Of Variance ◽  
The Impact

Signal detection theory is commonly utilized in the field of human factors. Despite its common use, the assessment of the signal detection theory assumptions is not often cited. The purpose of this research was to provide a preliminary assessment of the impact of assumption violations on estimates of sensitivity commonly used in signal detection theory research. This assessment was performed using Monte Carlo simulations. Our research indicated that violating the homogeneity of variance assumption resulted in estimates of sensitivity varying with changes in the response criterion. However, an unequal number of signal and noise trials, which is common in vigilance research, did not impact the estimates of sensitivity. Based upon our findings, caution should be taken with regard to violations of homogeneity of variance. Future research aims to determine the impact of multiple assumption violations on estimates of sensitivity.

Probabilistic modeling of sewer deterioration using inspection data

2008 ◽  
Vol 57 (10) ◽  
pp. 1635-1641 ◽  
Author(s):  
J. Dirksen ◽  
F. H. L. R. Clemens
Keyword(s):  
Visual Inspection ◽  
Surface Damage ◽  
Mechanical Action ◽  
Sewer System ◽  
Sewer Systems ◽  
Historical Databases ◽  
Inspection Data ◽  
Need For Rehabilitation ◽  
Sewer Inspection

Accurate prediction of current and future conditions of sewer systems is crucial to manage the sewer system wisely, cost-effectively and efficiently. The application of historical databases of visual inspection data to sewer deterioration modeling seems common sense. However, in The Netherlands, sewer inspection data is only used to determine the direct need for rehabilitation. This paper outlines the possibilities of using inspection data for deterioration modeling and discusses the problems encountered. A case study was performed on the modeling of the condition aspect ‘surface damage by corrosion or mechanical action’ using a Markov model.

scholarly journals Model predictive control based on artificial intelligence and EPA-SWMM model to reduce CSOs impacts in sewer systems

2021 ◽  
Author(s):  
Khalid El Ghazouli ◽  
Jamal El Khatabi ◽  
Aziz Soulhi ◽  
Isam Shahrour
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Control Strategies ◽  
Dynamic Control ◽  
Combined Sewer Overflows ◽  
Sewer System ◽  
Sewer Systems ◽  
Proposed Model ◽  
The Impact ◽  
The City

Abstract Urbanization and an increase in precipitation intensities due to climate change, in addition to limited urban drainage systems (UDS) capacity, are the main causes of combined sewer overflows (CSOs) that cause serious water pollution problems in many cities around the world. Model predictive control (MPC) systems offer a new approach to mitigate the impact of CSOs by generating optimal temporally and spatially varied dynamic control strategies of sewer system actuators. This paper presents a novel MPC based on neural networks for predicting flows, a stormwater management model (SWMM) for flow conveyance, and a genetic algorithm for optimizing the operation of sewer systems and defining the best control strategies. The proposed model was tested on the sewer system of the city of Casablanca in Morocco. The results have shown the efficiency of the developed MPC to reduce CSOs while considering short optimization time thanks to parallel computing.

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