Application of Multiple Wave Model Attenuation Technology Based on Travel-time in the Carboniferous Strata in the Hinterland of Junggar Basin

2018 ◽  
Author(s):  
Xiao Yanling ◽  
Yang Xiaohai ◽  
He Luming ◽  
Su Yanli ◽  
Chen Jinliang
Keyword(s):  
Travel Time ◽  
Junggar Basin ◽  
Wave Model ◽  
Multiple Wave

scholarly journals Regional Downscaling of Copernicus ERA5 Wave Data for Coastal Engineering Activities and Operational Coastal Services

Water ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 859
Author(s):  
Giorgio Bellotti ◽  
Leopoldo Franco ◽  
Claudia Cecioni
Keyword(s):  
Time Series ◽  
National Level ◽  
Wave Model ◽  
Wave Climate ◽  
Tyrrhenian Sea ◽  
Computational Domain ◽  
Multiple Wave ◽  
Space Resolution ◽  
Wave Data ◽  
Fundamental Information

Hindcasted wind and wave data, available on a coarse resolution global grid (Copernicus ERA5 dataset), are downscaled by means of the numerical model SWAN (simulating waves in the nearshore) to produce time series of wave conditions at a high resolution along the Italian coasts in the central Tyrrhenian Sea. In order to achieve the proper spatial resolution along the coast, the finite element version of the model is used. Wave data time series at the ERA5 grid are used to specify boundary conditions for the wave model at the offshore sides of the computational domain. The wind field is fed to the model to account for local wave generation. The modeled sea states are compared against the multiple wave records available in the area, in order to calibrate and validate the model. The model results are in quite good agreement with direct measurements, both in terms of wave climate and wave extremes. The results show that using the present modeling chain, it is possible to build a reliable nearshore wave parameters database with high space resolution. Such a database, once prepared for coastal areas, possibly at the national level, can be of high value for many engineering activities related to coastal area management, and can be useful to provide fundamental information for the development of operational coastal services.

scholarly journals Multiple Epidemic Wave Model of the COVID-19 Pandemic: Modeling Study

10.2196/20912 ◽  
2020 ◽  
Vol 22 (7) ◽  
pp. e20912 ◽  
Author(s):  
Efthimios Kaxiras ◽  
Georgios Neofotistos
Keyword(s):  
Wave Model ◽  
Disease Spread ◽  
Multiple Wave ◽  
Agent Based ◽  
Future Evolution ◽  
Response Index ◽  
Infected Population ◽  
Intervention Measures ◽  
Agent Based Simulations ◽  
Pandemic Response

Background Intervention measures have been implemented around the world to mitigate the spread of the coronavirus disease (COVID-19) pandemic. Understanding the dynamics of the disease spread and the effectiveness of the interventions is essential in predicting its future evolution. Objective The aim of this study is to simulate the effect of different social distancing interventions and investigate whether their timing and stringency can lead to multiple waves (subepidemics), which can provide a better fit to the wavy behavior observed in the infected population curve in the majority of countries. Methods We have designed and run agent-based simulations and a multiple wave model to fit the infected population data for many countries. We have also developed a novel Pandemic Response Index to provide a quantitative and objective way of ranking countries according to their COVID-19 response performance. Results We have analyzed data from 18 countries based on the multiple wave (subepidemics) hypothesis and present the relevant parameters. Multiple waves have been identified and were found to describe the data better. The effectiveness of intervention measures can be inferred by the peak intensities of the waves. Countries imposing fast and stringent interventions exhibit multiple waves with declining peak intensities. This result strongly corroborated with agent-based simulations outcomes. We also provided an estimate of how much lower the number of infections could have been if early and strict intervention measures had been taken to stop the spread at the first wave, as actually happened for a handful of countries. A novel index, the Pandemic Response Index, was constructed, and based on the model’s results, an index value was assigned to each country, quantifying in an objective manner the country’s response to the pandemic. Conclusions Our results support the hypothesis that the COVID-19 pandemic can be successfully modeled as a series of epidemic waves (subepidemics) and that it is possible to infer to what extent the imposition of early intervention measures can slow the spread of the disease.

Departure and travel time model for the temporal distribution of morning rush-hour traffic congestion

2019 ◽  
Vol 31 (02) ◽  
pp. 2050023
Author(s):  
Sida Luo
Keyword(s):  
Travel Time ◽  
Traffic Congestion ◽  
Temporal Distribution ◽  
Time Estimation ◽  
Wave Model ◽  
Model Parameters ◽  
Travel Time Estimation ◽  
Time Model ◽  
Departure Time ◽  
Departure Time Choice

The chronic traffic congestion undermines the level of satisfaction within a society. This study proposes a departure time model for estimating the temporal distribution of morning rush-hour traffic congestion over urban road networks. The departure time model is developed based on the point queue model that is used for estimating travel time. First, we prove the effectiveness of the travel time model (i.e. point queue), showing that it gives the same travel time estimation as the kinematic wave model does for a road with successive bottlenecks. Then, a variant of the bottleneck model is developed accordingly, aiming to capture travelers’ departure time choice for commute trips. The proposed departure time model relaxes a traditional assumption that the last commuter experiences the free flow travel time and considers travelers’ unwillingness of late arrivals for work. Numerical experiments show that the morning rush-hour generally starts at 7:29 am and ends at 8:46 am with a traffic congestion delay index (TCDI) of 2.164 for Beijing, China. Furthermore, the estimation of rush-hour start and end time is insensitive to most model parameters including the proportion of travelers who tend to arrive at work earlier than their schedules.

scholarly journals RANCANG BANGUN SISTEM PENANGKAP DAN PEMBACAAN GELOMBANG SEISMIK REFRAKSI UNTUK EKSPLORASI HIDROKARBON

2019 ◽  
Vol 5 (1) ◽  
pp. 33-36
Author(s):  
Maison Maison ◽  
Faizar Farid ◽  
Samsidar ◽  
Linda Handayani ◽  
Rustan ◽  
...  
Keyword(s):  
Data Acquisition ◽  
System Design ◽  
Travel Time ◽  
Wave Velocity ◽  
Wave Amplitude ◽  
Low Cost ◽  
Wave Model ◽  
Digital Signals ◽  
Seismic Measurement ◽  
Equipment System

The subsurface data acquisition in seismic eksploration usually using expensive equipment. In this work, a low-cost seismic equipment system has been developed for receive and record seismic wave. This system consist of mikrocontroller and software LabView that connected to PC. The subsurface low signals is recorded by geophone and through the amplifier instrument non-inverting. Then, the digital signals is prosessed by Mikrocontroller and visualize by LabView. Output of seismic measurement using this system design are travel time and amplitude. Travel time is used to modelling density and wave velocity to generate wave model. The expected model can give us value of density and wave velocity to obtain the anomaly. It has been tested using 1 geophone and successfully showed the wave, amplitude, and travel time.

Evaluation of the Complicated Storm Sewer Networks Design Method and the Rational Method Using a Risk-Based Simulation-Optimization Approach

2021 ◽  
Author(s):  
Jamal Mohammad Vali Samani ◽  
Sonia Sadeghi ◽  
Hossein Mohammad Vali Samani
Keyword(s):  
Risk Analysis ◽  
Travel Time ◽  
Return Period ◽  
Design Method ◽  
Wave Model ◽  
Rational Method ◽  
Optimization Approach ◽  
Damage Risk ◽  
Sewer Networks ◽  
Storm Sewer

Abstract In this study, optimal designs with minimum costs are obtained for various storm return periods. Then the risk analysis is used to determine the return period in which the design cost plus the damage risk cost is minimum. SWMM software was used to handle the simulation and the Network optimization was performed by using the genetic algorithm. The non-linear reservoir model to convert the rainfall into runoff and the dynamic wave model to perform the network hydraulic simulation in this software are utilized as a complicated simulation model. The results showed that the 10-year return-period storm in which the summation of the design and the damage risk costs are minimum is the one that should be selected. Also, the rational method of the software was applied as the simplest method of rainfall-runoff and the hydraulic calculations were performed using a Manning equation without considering the flow travel time. The results show that the return period of the risk analysis is the same as the first one whereas the total design costs are greater by 16.6%. Afterward, the classical rational method in which the flow travel time is considered was used to design the same network. The peak flows of the pipes were remarkably reduced, causing the design costs to be only 4.7% greater than the complicated precise method. It can be concluded that the simple classic rational method considering the flow travel time may be used in the design of storm sewer networks due to its acceptable accuracy and costs.

scholarly journals Modeling the Covid-19 Pandemic Response of the US States

2020 ◽  
Author(s):  
Georgios Neofotistos ◽  
Efthimios Kaxiras
Keyword(s):  
District Of Columbia ◽  
State Level ◽  
Wave Model ◽  
The State ◽  
Multiple Wave ◽  
Us States ◽  
Response Index ◽  
Intervention Measures ◽  
Pandemic Response ◽  
The Waves

AbstractBackgroundThe United States of America (USA) has been the country worst affected, in absolute terms, by the Covid-19 pandemic. The country comprises 50 states under a federal system. The impact of the pandemic has resulted in different responses at the state level, which are driven by differing intervention policies, demographics, connectedness and other factors. Understanding the dynamics of the Covid-19 pandemic at the state level is essential in predicting its future evolution.ObjectiveOur objective is to identify and characterize multiple waves of the pandemic by analyzing the reported infected population curve in each of the 50 US states. Based on the intensity of the waves, characterized by declining, stationary, or increasing strengths, each state’s response can be inferred and quantified.MethodsWe apply a recently proposed multiple-wave model to fit the infected population data for each state in USA, and use the proposed Pandemic Response Index to quantify their response to the Covid-19 pandemic.ResultsWe have analyzed reported infected cases from each one of the 50 USA states and the District of Columbia, based on the multiple-wave model, and present the relevant parameters. Multiple waves have been identified and this model is found to describe the data better. Each of the states can be classified into one of three distinct classes characterized by declining, increasing, or stationary strength of the waves following the initial one. The effectiveness of intervention measures can be inferred by the peak intensities of the waves, and states with similar population characteristics can be directly compared. We estimate how much lower the number of infections might have been, if early and strict intervention measures had been imposed to stop the disease spread at the first wave, as was the case for certain states. Based on our model’s results, we compute the value of the Pandemic Response Index, a recently introduced metric for quantifying in an objective manner the response to the pandemic.ConclusionsOur results reveal a series of epidemic waves, characterizing USA’s pandemic response at the state level, and also infer to what extent the imposition of early intervention measures could have had on the spread and impact of the disease. As of June 11, 2020, only 19 states and the District of Columbia (40% of the total) clearly exhibit declining trends in the numbers of reported infected cases, while 13 states exhibit stationary and 18 states increasing trends in the numbers of reported cases.

scholarly journals Multiple Epidemic Wave Model of the COVID-19 Pandemic: Modeling Study (Preprint)

2020 ◽  
Author(s):  
Efthimios Kaxiras ◽  
Georgios Neofotistos
Keyword(s):  
Wave Model ◽  
Disease Spread ◽  
Multiple Wave ◽  
Agent Based ◽  
Future Evolution ◽  
Response Index ◽  
Infected Population ◽  
Intervention Measures ◽  
Agent Based Simulations ◽  
Pandemic Response

BACKGROUND Intervention measures have been implemented around the world to mitigate the spread of the coronavirus disease (COVID-19) pandemic. Understanding the dynamics of the disease spread and the effectiveness of the interventions is essential in predicting its future evolution. OBJECTIVE The aim of this study is to simulate the effect of different social distancing interventions and investigate whether their timing and stringency can lead to multiple waves (subepidemics), which can provide a better fit to the wavy behavior observed in the infected population curve in the majority of countries. METHODS We have designed and run agent-based simulations and a multiple wave model to fit the infected population data for many countries. We have also developed a novel Pandemic Response Index to provide a quantitative and objective way of ranking countries according to their COVID-19 response performance. RESULTS We have analyzed data from 18 countries based on the multiple wave (subepidemics) hypothesis and present the relevant parameters. Multiple waves have been identified and were found to describe the data better. The effectiveness of intervention measures can be inferred by the peak intensities of the waves. Countries imposing fast and stringent interventions exhibit multiple waves with declining peak intensities. This result strongly corroborated with agent-based simulations outcomes. We also provided an estimate of how much lower the number of infections could have been if early and strict intervention measures had been taken to stop the spread at the first wave, as actually happened for a handful of countries. A novel index, the Pandemic Response Index, was constructed, and based on the model’s results, an index value was assigned to each country, quantifying in an objective manner the country’s response to the pandemic. CONCLUSIONS Our results support the hypothesis that the COVID-19 pandemic can be successfully modeled as a series of epidemic waves (subepidemics) and that it is possible to infer to what extent the imposition of early intervention measures can slow the spread of the disease.

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