Buoyancy-driven motion of a deformable drop toward a planar wall at low Reynolds number

The slow viscous motion of a deformable drop moving normal to a planar wall is studied numerically. In particular, a boundary integral technique employing the Green's function appropriate to a no-slip planar wall is used. Beginning with spherical drop shapes far from the wall, highly deformed and ‘dimpled’ drop configurations are obtained as the planar wall is approached. The initial stages of dimpling and their evolution provide information and insight into the basic assumptions of film-drainage theory

A genetic algorithm for optimizing off-farm irrigation scheduling

This paper examines the use of genetic algorithm (GA) optimization to identify water delivery schedules for an open-channel irrigation system. Significant objectives and important constraints are identified for this system, and suitable representations of these within the GA framework are developed. Objectives include maximizing the number of orders that are scheduled to be delivered at the requested time and minimizing variations in the channel flow rate. If, however, an order is to be shifted, the irrigator preference for this to be by ±24 h rather than ±12 h is accounted for. Constraints include avoiding exceedance of channel capacity. The GA approach is demonstrated for an idealized system of five irrigators on a channel spur. In this case study, the GA technique efficiently identified the optimal schedule that was independently verified using full enumeration of the entire search space of possible order schedules. Results have shown great promise in the ability of GA techniques to identify good irrigation order schedules.J. B. Nixon, G. C. Dandy and A. R. Simpso

Scenario driven optimal sequencing under deep uncertainty

Abstract not availableEva H.Y. Beh, Holger R. Maier, Graeme C. Dand

Critical values of a kernel density-based mutual information estimator

Copyright © 2006 IEEERecently, mutual information (MI) has become widely recognized as a statistical measure of dependence that is suitable for applications where data are non-Gaussian, or where the dependency between variables is non-linear. However, a significant disadvantage of this measure is the inability to define an analytical expression for the distribution of MI estimators, which are based upon a finite dataset. This paper deals specifically with a popular kernel density based estimator, for which the distribution is determined empirically using Monte Carlo simulation. The application of the critical values of MI derived from this distribution to a test for independence is demonstrated within the context of a benchmark input variable selection problem.http://www.okstate.edu/elec-engr/faculty/yen/wcci/WCCI-Web_ProgramList_F.htm

Parameterization of a Gridded Rainfall-Runoff Model for Southern Australia

Source: ICHE Conference Archive - https://mdi-de.baw.de/icheArchiv

Predictors of Support Needs of Distance Education Students in the Institute of Distance Education and e-Learning (IDeL), University of Education, Winneba, Ghana

The study sought to establish the predictors of support systems for students in distance learning. Using the pragmatist paradigm, the study employed the concurrent triangulation design where 623 distance education students were randomly sampled from 41 study centers in Ghana for the quantitative phase of the study. Concurrently, 18 participated in 3 separate focus group discussions made up of 6 students each whilst the Registrar was also interviewed. Questionnaire adapted from Ozoglu (2009) was used to collect quantitative data whilst self-constructed semi-structured interview guides were used to collect qualitative data from the respondents. Means and Standard Deviations as well as Multiple Regression were used to analyse the quantitative data. The qualitative data were analysed using content analysis for respondents’ interview data. The study found that the most critical learner support needs were assistance in overcoming technical problems, orientation to the course media/delivery format of IDeL, help with the admission/registration process, counseling services to overcome students’ concerns about their education and textbooks provided by IDeL. The Regression Analysis showed that the learner support needs of the distance education students were predicted by sex, age and certificates students’ have enrolled for. It was recommended, therefore, that IDeL should consider the sex, age and certificates students’ have enrolled for in the provision of support systems for their students as they predict the support needs of the students

Real-time deployment of artificial neural network forecasting models: Understanding the range of applicability

Extent: 16p.When an operational artificial neural network (ANN) model is deployed, new input patterns are collected in order to make real-time forecasts. However, ANNs (like other empirical and statistical methods) are unable to reliably extrapolate beyond the calibration range. Consequently, when deployed in real-time operation there is a need to determine if new input patterns are representative of the data used in calibrating the model. To address this problem, a novel detection system for identifying uncharacteristic data patterns is presented. This approach combines a self-organizing map (SOM), to partition the data set, with nonparametric kernel density estimators to calculate local density estimates (LDE). The SOM-LDE method determines the degree to which a new input pattern can be considered to be contained within the domain of the calibration set. If a new pattern is found to be uncharacteristic, a warning can be issued with the forecast, and the ANN model retrained to include the new pattern. This approach of selectively retraining the model is compared to no retraining and the more computationally onerous case of retraining the model after each new sample. These three approaches are applied to forecast flow in the Kentucky River, USA, using multilayer perceptron (MLP) models. The results demonstrate that there is a significant advantage in retraining an ANN that has been deployed as a real-time, operational model, and that the SOM-LDE classifier is an effective approach for identifying the model's range of applicability and assessing the usefulness of the forecast.Gavin J. Bowden, Holger R. Maier, and Graeme C. Dand

A framework for using ant colony optimization to schedule environmental flow management alternatives for rivers, wetlands, and floodplains

[1] Rivers, wetlands, and floodplains are in need of management as they have been altered from natural conditions and are at risk of vanishing because of river development. One method to mitigate these impacts involves the scheduling of environmental flow management alternatives (EFMA); however, this is a complex task as there are generally a large number of ecological assets (e.g., wetlands) that need to be considered, each with species with competing flow requirements. Hence, this problem evolves into an optimization problem to maximize an ecological benefit within constraints imposed by human needs and the physical layout of the system. This paper presents a novel optimization framework which uses ant colony optimization to enable optimal scheduling of EFMAs, given constraints on the environmental water that is available. This optimization algorithm is selected because, unlike other currently popular algorithms, it is able to account for all aspects of the problem. The approach is validated by comparing it to a heuristic approach, and its utility is demonstrated using a case study based on the Murray River in South Australia to investigate (1) the trade-off between plant recruitment (i.e., promoting germination) and maintenance (i.e., maintaining habitat) flow requirements, (2) the trade-off between flora and fauna flow requirements, and (3) a hydrograph inversion case. The results demonstrate the usefulness and flexibility of the proposed framework as it is able to determine EFMA schedules that provide optimal or near-optimal trade-offs between the competing needs of species under a range of operating conditions and valuable insight for managers.J.M. Szemis, H.R. Maier and G.C. Dand

Adaptive, multiobjective optimal sequencing approach for urban water supply augmentation under deep uncertainty

Optimal long-term sequencing and scheduling play an important role in many water resources problems. The optimal sequencing of urban water supply augmentation options is one example of this. In this paper, an adaptive, multiobjective optimal sequencing approach for urban water supply augmentation under deep uncertainty is introduced. As part of the approach, optimal long-term sequence plans are updated at regular intervals and trade-offs between the robustness and flexibility of the solutions that have to be fixed at the current time and objectives over the entire planning horizon are considered when selecting the most appropriate course of action. The approach is demonstrated for the sequencing of urban water supply augmentation options for the southern Adelaide water supply system for two assumed future realities. The results demonstrate the utility of the proposed approach, as it is able to identify optimal sequences that perform better than those obtained using static approaches.Eva H.Y. Beh, Holger R. Maier, and Graeme C. Dand

Pipe network optimisation using genetic algorithms

A new optimization technique of generic algorithms has recently been successfully applied to pipe network optimization. In this paper a parametric analysis is carried out of the genetic algorithm in order to assess the form of the fitness function.Simpson A, Murphy L, Dandy Ghttp://trove.nla.gov.au/version/4543312