Near-field modeling of the Mamala Bay outfalls

1995 ◽  
Vol 32 (2) ◽  
pp. 159-166 ◽  
Author(s):  
Philip J. W. Roberts
Keyword(s):  
Coastal Waters ◽  
Input Data ◽  
Near Field ◽  
Density Stratification ◽  
Statistical Characteristics ◽  
Considerable Variability ◽  
Field Modeling ◽  
Long Time ◽  
The Mathematical Model ◽  
Acoustic Doppler Current Profilers

Preliminary results of near-field modeling of the wastefield formed by the Sand Island, Honolulu, outfall are presented. Over one thousand simulations were run with the mathematical model RSB using as input data long time series of effluent flowrate and oceanographic observations over the whole water column including currents measured by Acoustic Doppler Current Profilers (ADCPs), and density stratification measured by thermistor strings. It was found that considerable variability in plume behavior occurs, whose extremes depend on particular combinations of flowrate, current, and density stratification. The wastefield is predicted to be submerged, usually deeply, about 90% of the time for the conditions simulated. It is demonstrated that the use of these recently developed instruments combined with appropriate mathematical models can lead to greatly improved predictions of the statistical characteristics of wastefield behavior in coastal waters than has been previously possible.

Far field modeling of the Mamala Bay outfalls

1998 ◽  
Vol 38 (10) ◽  
pp. 323-330
Author(s):  
Philip J. W. Roberts
Keyword(s):  
Field Model ◽  
Near Field ◽  
Far Field ◽  
Field Modeling ◽  
Visitation Frequency ◽  
Statistical Variation ◽  
Long Time ◽  
Harmonic Average ◽  
Ocean Outfall ◽  
Acoustic Doppler Current Profilers

The results of far field modeling of the wastefield formed by the Sand Island, Honolulu, ocean outfall are presented. A far field model, FRFIELD, was coupled to a near field model, NRFIELD. The input data for the models were long time series of oceanographic observations over the whole water column including currents measured by Acoustic Doppler Current Profilers and density stratification measured by thermistor strings. Thousands of simulations were made to predict the statistical variation of wastefield properties around the diffuser. It was shown that the visitation frequency of the wastefield decreases rapidly with distance from the diffuser. The spatial variation of minimum and harmonic average dilutions was also predicted. Average dilution increases rapidly with distance. It is concluded that any impact of the discharge will be confined to a relatively small area around the diffuser and beach impacts are not likely to be significant.

The Spread of Pollutants from Harbour Sludge Depots

1984 ◽  
Vol 16 (3-4) ◽  
pp. 623-633
Author(s):  
M Loxham ◽  
F Weststrate
Keyword(s):  
Time Scales ◽  
Molecular Diffusion ◽  
Near Field ◽  
Parameter Analysis ◽  
Migration Patterns ◽  
Long Time ◽  
Dilution Effects ◽  
Short Time ◽  
Harbour Sludge

It is generally agreed that both the landfill option, or the civil techniques option for the final disposal of contaminated harbour sludge involves the isolation of the sludge from the environment. For short time scales, engineered barriers such as a bentonite screen, plastic sheets, pumping strategies etc. can be used. However for long time scales the effectiveness of such measures cannot be counted upon. It is thus necessary to be able to predict the long term environmenttal spread of contaminants from a mature landfill. A model is presented that considers diffusion and adsorption in the landfill site and convection and adsorption in the underlaying aquifer. From a parameter analysis starting form practical values it is shown that the adsorption behaviour and the molecular diffusion coefficient of the sludge, are the key parameters involved in the near field. The dilution effects of the far field migration patterns are also illustrated.

Research on cargo-loading optimization based on genetic and fuzzy integration

2021 ◽  
Vol 40 (4) ◽  
pp. 8493-8500
Author(s):  
Yanwei Du ◽  
Feng Chen ◽  
Xiaoyi Fan ◽  
Lei Zhang ◽  
Henggang Liang
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Three Dimensional ◽  
Distribution Center ◽  
Long Time ◽  
Low Efficiency ◽  
Decision Making Model ◽  
The Mathematical Model ◽  
Loading Scheme

With the increase of the number of loaded goods, the number of optional loading schemes will increase exponentially. It is a long time and low efficiency to determine the loading scheme with experience. Genetic algorithm is a search heuristic algorithm used to solve optimization in the field of computer science artificial intelligence. Genetic algorithm can effectively select the optimal loading scheme but unable to utilize weight and volume capacity of cargo and truck. In this paper, we propose hybrid Genetic and fuzzy logic based cargo-loading decision making model that focus on achieving maximum profit with maximum utilization of weight and volume capacity of cargo and truck. In this paper, first of all, the components of the problem of goods stowage in the distribution center are analyzed systematically, which lays the foundation for the reasonable classification of the problem of goods stowage and the establishment of the mathematical model of the problem of goods stowage. Secondly, the paper abstracts and defines the problem of goods loading in distribution center, establishes the mathematical model for the optimization of single car three-dimensional goods loading, and designs the genetic algorithm for solving the model. Finally, Matlab is used to solve the optimization model of cargo loading, and the good performance of the algorithm is verified by an example. From the performance evaluation analysis, proposed the hybrid system achieve better outcomes than the standard SA model, GA method, and TS strategy.

Self-diffusion of bimodal suspensions of hydrodynamically interacting spherical particles in shearing flow

1994 ◽  
Vol 281 ◽  
pp. 51-80 ◽  
Author(s):  
Chingyi Chang ◽  
Robert L. Powell
Keyword(s):  
Diffusion Coefficients ◽  
Cluster Size ◽  
Near Field ◽  
Spherical Particles ◽  
Random Structure ◽  
Shearing Flow ◽  
Self Diffusion ◽  
Long Time ◽  
Mobility Matrix ◽  
Many Body Interactions

We study the average mobilities and long-time self-diffusion coefficients of a suspension of bimodally distributed spherical particles. Stokesian dynamics is used to calculate the particle trajectories for a monolayer of bimodal-sized spheres. Hydrodynamic forces only are considered and they are calculated using the inverse of the grand mobility matrix for far-field many-body interactions and lubrication formulae for near-field effects. We determine both the detailed microstructure (e.g. the pair-connectedness function and cluster formation) and the macroscopic properties (e.g. viscosity and self-diffusion coefficients). The flow of an ‘infinite’ suspension is simulated by considering 25, 49, 64 and 100 particles to be one ‘cell’ of a periodic array. Effects of both the size ratio and the relative fractions of the different-sized particles are examined. For the microstructures, the pair-connectedness function shows that the particles form clusters in simple shearing flow due to lubrication forces. The nearly symmetric angular structures imply the absence of normal stress differences for a suspension with purely hydrodynamic interactions between spheres. For average mobilities at infinite Péclet number, Ds0, our simulation results suggest that the reduction of Ds0 as concentration increases is directly linked to the influence of particle size distribution on the average cluster size. For long-time self-diffusion coefficients, Ds∞, we found good agreement between simulation and experiment (Leighton & Acrovos 1987 a; Phan and Leighton 1993) for monodispersed suspensions. For bimodal suspensions, the magnitude of Ds∞, and the time to reach the asymptotic diffusive behaviour depend on the cluster size formed in the system, or the viscosity of the suspension. We also consider the effect of the initial configuration by letting the spheres be both organized (size segregated) and randomly placed. We find that it takes a longer time for a suspension with an initially organized structure to achieve steady state than one with a random structure.

scholarly journals AGV Brake System Simulation

2019 ◽  
Vol 10 (1) ◽  
pp. 1-9 ◽  
Author(s):  
Daniel Varecha ◽  
Robert Kohar ◽  
Frantisek Brumercik
Keyword(s):  
Mathematical Model ◽  
Input Data ◽  
Initial Conditions ◽  
Brake System ◽  
Disc Brake ◽  
Hydraulic Control ◽  
Stopping Distance ◽  
Braking Force ◽  
Braking Process ◽  
The Mathematical Model

Abstract The article is focused on braking simulation of automated guided vehicle (AGV). The brake system is used with a disc brake and with hydraulic control. In the first step, the formula necessary for braking force at the start of braking is derived. The stopping distance is 1.5 meters. Subsequently, a mathematical model of braking is created into which the formula of the necessary braking force is applied. The mathematical model represents a motion equation that is solved in the software Matlab by an approximation method. Next a simulation is created using Matlab software and the data of simulation are displayed in the graph. The transport speed of the vehicle is 1 〖m.s〗^(-1) and the weight of the vehicle is 6000 kg including load. The aim of this article is to determine the braking time of the device depending from the input data entered, which represent the initial conditions of the braking process.

scholarly journals THE MATHEMATICAL MODEL FOR ESTIMATING THE PROBABILITY OF RISK OF INCOMPETENT SPECIALIST GRADUATION

2020 ◽  
Author(s):  
Ganna Khimicheva ◽  
◽  
Antonina Volivach ◽  
Keyword(s):  
Mathematical Model ◽  
Educational Program ◽  
Pairwise Comparison ◽  
Educational Programs ◽  
Educational Process ◽  
Program Quality ◽  
Statistical Characteristics ◽  
Multidimensional Space ◽  
Standards And Guidelines ◽  
The Mathematical Model

The article presents the results of research for the mathematical model for estimating the probability of risk of incompetent specialist graduation. The mechanisms and tools to determine the probability of risk of incompetent specialist graduation have been developed in the course of the research. The goal tree method has been used as a mechanism to determine the relationship between the structural components of the educational process and the educational program. Using this method, the structuring of 9 criteria by which the educational program quality is evaluated has been carried out. That is, its strengths and weaknesses have been identified. In turn, as a tool for estimating the probability of an educational process (educational program) risks and the graduation of an incompetent specialist, it has been proposed to use a regression mathematical model. To build a mathematical model, an active experiment, a qualimetric approach, a method of regression analysis, and 16 conditional educational programs that met the "Standards and Guidelines for Quality Assurance in the European Higher Education Area" (ESG) have been used. The construction of the model has been carried out according to a specially designed scheme, which included 5 stages. In the first stage, a group of experts was formed and their consistency was determined with the help of the "HEI Experts" software. In the second stage, six groups of indicators were identified, which further estimated the educational process (educational program) quality. For this purpose, the experts used the method of pairwise comparison to select 9 unit indicators, which further estimated the levels of compliance of 16 conditional educational programs. The estimation was conducted according to standardized quality indicators that are inherent in the real educational process (educational program). In the third stage, a robust plan of the experiment was constructed using the method of pseudo-random LP-τ numbers uniformly distributed in multidimensional space. According to the plan, a working matrix of the experiment was formed. Then, the group of experts formed in the first stage carried out the percentage estimation of the probability of risk of incompetent specialist graduation. In the fourth stage, a mathematical model was built using the PRIAM (planning, regression, and model analysis) software. This model allows us to assess nine factors that affect the probability of risk of incompetent specialist graduation. In the fifth stage, the statistical characteristics of the model were tested. According to the test results, it was proved that the model is informative, adequate, and stable, both in terms of structure and calculations. At the same stage, the marginal surfaces were constructed and the forces of influence of regressors (indicators) on the probability of risk of competent/incompetent specialist graduation were determined. According to the results of research, it has been proved that such indicators as compliance of the applicants (bachelors) level with the second Master's level and the level of considering labor market employers (stakeholders) requirements have the strongest impact on the competence of the future specialists. The proposed model allows us to estimate the factors influencing the efficiency (effectiveness) of the educational process and to determine the probability of the risk of competent/incompetent specialist graduation.

scholarly journals Nurse Scheduling Using Genetic Algorithm

2014 ◽  
Vol 2014 ◽  
pp. 1-16 ◽  
Author(s):  
Komgrit Leksakul ◽  
Sukrit Phetsawat
Keyword(s):  
Genetic Algorithm ◽  
Simulation Model ◽  
Input Data ◽  
Level Of Service ◽  
Nurse Scheduling ◽  
Random Mutation ◽  
Scheduling Model ◽  
Hospital Staffing ◽  
Service Standards ◽  
The Mathematical Model

This study applied engineering techniques to develop a nurse scheduling model that, while maintaining the highest level of service, simultaneously minimized hospital-staffing costs and equitably distributed overtime pay. In the mathematical model, the objective function was the sum of the overtime payment to all nurses and the standard deviation of the total overtime payment that each nurse received. Input data distributions were analyzed in order to formulate a simulation model to determine the optimal demand for nurses that met the hospital’s service standards. To obtain the optimal nurse schedule with the number of nurses acquired from the simulation model, we proposed a genetic algorithm (GA) with two-point crossover and random mutation. After running the algorithm, we compared the expenses and number of nurses between the existing and our proposed nurse schedules. For January 2013, the nurse schedule obtained by GA could save 12% in staffing expenses per month and 13% in number of nurses when compare with the existing schedule, while more equitably distributing overtime pay between all nurses.

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