Optimal Design of Pumped Pipeline Systems Using Genetic Algorithm and Mathematical Optimization

In recent years, much attention has been paid to the optimal design of pipeline systems. In this study, the problem of pipeline system optimal design has been solved through genetic algorithm and mathematical optimization. Pipe diameters and their thicknesses are considered as decision variables to be designed in a manner that water column separation and excessive pressures are avoided in the event of pump failure. Capabilities of the genetic algorithm and the mathematical programming method are compared for the problem under consideration. For simulation of transient streams, explicit characteristic method is used in which devices such as pumps are defined as boundary conditions of the equations defining the hydraulic behavior of pipe segments. The problem of optimal design of pipeline systems is a constrained problem which is converted to an unconstrained optimization problem using an external penalty function approach. The efficiency of the proposed approaches is verified in one example and the results are presented

Prediction of scour depth at breakwaters due to non-breaking waves using machine learning approaches

Coastal structures may cease to function properly due to seabed scouring. Hence, prediction of the maximum scour depth is of great importance for the protection of these structures. Since scour is the result of a complicated interaction between structure, sediment, and incoming waves, empirical equations are notas accurate as machine learning schemes, which are being widely employed for the coastal engineering modeling. In this paper, which can be regarded as an extension of Pourzangbar et al. (2016), two soft computing methods, a support vector regression (SVR), and a model tree algorithm (M5'), have been implemented to predict the maximum scour depth due to non-breaking waves. The models predict therelative scour depth (Smax/H0) on the basis of the following variables: relative water depth at the toe ofthe breakwater (htoe/L0), Shields parameter (theta), non-breaking wave steepness (H0/L0), and reflection coef-ficient (Cr). 95 laboratory data points, extracted from dedicated experimental studies, have been used for developing the models, whose performances have been assessed on the basis of statistical parameters.The results suggest that all of the developed models predict the maximum scour depth with high preci-sion, the M5model performed marginally better than the SVR model and also allowed to define a set oftransparent and physically sound relationships. Such relationships, which are in good agreement withthe existing empirical findings, show that the relative scour depth is mainly affected by wave reflection

An economic impact metric for evaluating wave height forecasters for offshore wind maintenance access

This paper demonstrates that wave height forecasters chosen on statistical quality metrics result in sub-optimal decision support for offshore wind farm maintenance. Offshore access is constrained by wave height, but the majority of approaches to evaluating the effectiveness of a wave height forecaster utilize overall accuracy or error rates. This paper introduces a new metric more appropriate to the wind industry, which considers the economic impact of an incorrect forecast above or below critical wave height boundaries. The paper describes a process for constructing a value criteria where the implications between forecasting error and economic consequences are explicated in terms of opportunity costs and realized maintenance costs. A comparison between nine forecasting techniques for modeling and predicting wave heights based on historical data, including an ensemble aggregator, is described demonstrating that the performance ranking of forecasters is sensitive to the evaluation criteria. The results highlight the importance of appropriate metrics for wave height prediction specific to the wind industry, and the limitations of current models that minimize a metric that does not support decision making. With improved ability to forecast weather windows, maintenance scheduling is subject to less uncertainty, hence reducing costs related to vessel dispatch, and lost energy due to downtime