scholarly journals Computer Deterministic Modelling of Nuclear Problems using Nodal Methods

2017 ◽  
Author(s):  
Amaury Muñoz Oliva ◽  
Hermes Alves Filho ◽  
Davi José Martins e Silva ◽  
Carlos Rafael Garcı́a Hernández
Keyword(s):  
Deterministic Modelling ◽  
Nodal Methods

Incorporating concepts from physical theory into stochastic modelling of urban runoff pollution

1998 ◽  
Vol 37 (1) ◽  
pp. 179-185
Author(s):  
Morten Grum
Keyword(s):  
Stochastic Model ◽  
Continuous Time ◽  
Measurement Errors ◽  
Physical Theory ◽  
Oxygen Demand ◽  
Stochastic Modelling ◽  
Urban Runoff ◽  
Primary Input ◽  
Deterministic Modelling ◽  
Runoff Pollution

On evaluating the present or future state of integrated urban water systems, sewer drainage models, with rainfall as primary input, are often used to calculate the expected return periods of given detrimental acute pollution events and the uncertainty thereof. The model studied in the present paper incorporates notions of physical theory in a stochastic model of water level and particulate chemical oxygen demand (COD) at the overflow point of a Dutch combined sewer system. A stochastic model based on physical mechanisms has been formulated in continuous time. The extended Kalman filter has been used in conjunction with a maximum likelihood criteria and a non-linear state space formulation to decompose the error term into system noise terms and measurement errors. The bias generally obtained in deterministic modelling, by invariably and often inappropriately assuming all error to result from measurement inaccuracies, is thus avoided. Continuous time stochastic modelling incorporating physical, chemical and biological theory presents a possible modelling alternative. These preliminary results suggest that further work is needed in order to fully appreciate the method's potential and limitations in the field of urban runoff pollution modelling.

Hybrid machine learning process improves operations in South Australian trial

2021 ◽  
Vol 5 (4) ◽  
pp. 1-16
Author(s):  
Kalan Braun ◽  
James Ireland ◽  
Jonathan Klaric
Keyword(s):  
Machine Learning ◽  
Asset Management ◽  
South Australia ◽  
Cloud Data ◽  
Engineering Teams ◽  
Deterministic Modelling ◽  
Full Production ◽  
Machine Learning Applications ◽  
Hybrid Machine ◽  
One Year

TRILITY has developed a production cloud data management solution, KOIOS DatalytiX to provide hybrid machine learning applications and deterministic modelling tools to its operations, asset management, and engineering teams. Two optimiser applications for chemical coagulant prediction and filtration performance were deployed in the Riverland, South Australia at 10 conventional water treatment plants in 2018, to full production in 2019. One year later the impacts on the optimisation behaviour of operations teams, process performance and trihalomethane (THM) reduction outcomes are discussed.

scholarly journals A TROPICAL CYCLONE WIND, WAVE AND STORM TIDE RISK DESIGN AND WARNING TOOLBOX FOR AUSTRALIA

2020 ◽  
pp. 5
Author(s):  
Bruce Harper ◽  
Luciano Mason
Keyword(s):  
Tropical Cyclone ◽  
Emergency Management ◽  
Wind Wave ◽  
Rapid Assessment ◽  
Future Climate Change ◽  
Probabilistic Design ◽  
Storm Tide ◽  
Detailed Design ◽  
Deterministic Modelling ◽  
Software Toolbox

The paper will describe the development and operation of a comprehensive tropical cyclone wind, wave and storm tide risk design and warning software toolbox that covers all of tropical-influenced Australia, The toolbox is designed to provide both rapid assessment and detailed design capabilities for coastal, port, offshore and nearshore design, including projected future climate change. It also provides real-time emergency management functionality. The toolbox provides a probabilistic design framework that facilitates the essential need for sensitivity analysis of both inputs and outputs without pre-conceived risk thresholds. It provides the essential hazard component in a robust and verified context that can then, depending on the application, enable more focused and efficient deterministic modelling stages using models of choice.Recorded Presentation from the vICCE (YouTube Link): https://youtu.be/pssgiYdkEbM

Development and verification of new nodal methods in the KIKO3DMG code

Kerntechnik ◽  
2014 ◽  
Vol 79 (4) ◽  
pp. 314-322 ◽  
Author(s):  
I. Pataki ◽  
A. Keresztúri
Keyword(s):  
Nodal Methods
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