Improved air valve design using evolutionary polynomial regression

2019 ◽  
Vol 19 (7) ◽  
pp. 2036-2043
Author(s):  
G. Balacco ◽  
D. Laucelli
Keyword(s):  
Polynomial Regression ◽  
Pipeline System ◽  
Heuristic Methods ◽  
Data Modelling ◽  
Valve Design ◽  
Technical Literature ◽  
Evolutionary Polynomial Regression ◽  
Physically Based ◽  
Air Valve ◽  
Modelling Approach

Abstract Air valves are usually sized by heuristic methods or, sometimes, even oversized. Although the technical literature has long focused on the correct sizing of air valves to reduce the overpressure generated by the filling of a pipe, the phenomenon is complex and does not seem to be representable by physically based equations in an easy way, to be of practical use for technicians and designers. In this paper, air valve design is approached through an alternative data-modelling approach, based on evolutionary polynomial regression, with the aim to provide symbolic formulas of variable complexity and accuracy, suitable for physical interpretation, and at the same time easy to be used and applied for design purposes. The present investigation suggests a design formula that, given the geometric parameters of the pipeline system where the air valve is installed, provides the maximum tolerable overpressure, thus allowing the optimal air valve orifice size to be identified.

scholarly journals Asset deterioration analysis using multi-utility data and multi-objective data mining

2009 ◽  
Vol 11 (3-4) ◽  
pp. 211-224 ◽  
Author(s):  
D. A. Savic ◽  
O. Giustolisi ◽  
D. Laucelli
Keyword(s):  
Data Mining ◽  
Polynomial Regression ◽  
Single Case ◽  
Model Parameters ◽  
Physical Systems ◽  
Multi Objective ◽  
Evolutionary Polynomial Regression ◽  
Physically Based ◽  
Physical Laws

Physically-based models derive from first principles (e.g. physical laws) and rely on known variables and parameters. Because these have physical meaning, they also explain the underlying relationships of the system and are usually transportable from one system to another as a structural entity. They only require model parameters to be updated. Data-driven or regressive techniques involve data mining for modelling and one of the major drawbacks of this is that the functional form describing relationships between variables and the numerical parameters is not transportable to other physical systems as is the case with their classical physically-based counterparts. Aimed at striking a balance, Evolutionary Polynomial Regression (EPR) offers a way to model multi-utility data of asset deterioration in order to render model structures transportable across physical systems. EPR is a recently developed hybrid regression method providing symbolic expressions for models and works with formulae based on pseudo-polynomial expressions, usually in a multi-objective scenario where the best Pareto optimal models (parsimony versus accuracy) are selected from data in a single case study. This article discusses the improvement of EPR in dealing with multi-utility data (multi-case study) where it has been tried to achieve a general model structure for asset deterioration prediction across different water systems.

scholarly journals Evaluation of Induced Settlements of Piled Rafts in the Coupled Static-Dynamic Loads Using Neural Networks and Evolutionary Polynomial Regression

2017 ◽  
Vol 2017 ◽  
pp. 1-23 ◽  
Author(s):  
Ali Ghorbani ◽  
Mostafa Firouzi Niavol
Keyword(s):  
Neural Networks ◽  
Finite Difference ◽  
Polynomial Regression ◽  
Three Dimensional ◽  
Dynamic Loads ◽  
Prototype Model ◽  
Evolutionary Polynomial Regression ◽  
Dimensional Finite Element ◽  
Heavy Loads ◽  
Three Dimensional Finite Element

Coupled Piled Raft Foundations (CPRFs) are broadly applied to share heavy loads of superstructures between piles and rafts and reduce total and differential settlements. Settlements induced by static/coupled static-dynamic loads are one of the main concerns of engineers in designing CPRFs. Evaluation of induced settlements of CPRFs has been commonly carried out using three-dimensional finite element/finite difference modeling or through expensive real-scale/prototype model tests. Since the analyses, especially in the case of coupled static-dynamic loads, are not simply conducted, this paper presents two practical methods to gain the values of settlement. First, different nonlinear finite difference models under different static and coupled static-dynamic loads are developed to calculate exerted settlements. Analyses are performed with respect to different axial loads and pile’s configurations, numbers, lengths, diameters, and spacing for both loading cases. Based on the results of well-validated three-dimensional finite difference modeling, artificial neural networks and evolutionary polynomial regressions are then applied and introduced as capable methods to accurately present both static and coupled static-dynamic settlements. Also, using a sensitivity analysis based on Cosine Amplitude Method, axial load is introduced as the most influential parameter, while the ratio l/d is reported as the least effective parameter on the settlements of CPRFs.

scholarly journals Comparison of Different Approaches to Predict the Performance of Pumps As Turbines (PATs)

Energies ◽  
2018 ◽  
Vol 11 (4) ◽  
pp. 1016 ◽  
Author(s):  
Mauro Venturini ◽  
Stefano Alvisi ◽  
Silvio Simani ◽  
Lucrezia Manservigi
Keyword(s):  
Simulation Model ◽  
Ad Hoc ◽  
Polynomial Regression ◽  
Point Of View ◽  
Box Model ◽  
Physical Point ◽  
Evolutionary Polynomial Regression ◽  
Box Models ◽  
Performance Curves ◽  
Physics Based Simulation

This paper deals with the comparison of different methods which can be used for the prediction of the performance curves of pumps as turbines (PATs). The considered approaches are four, i.e., one physics-based simulation model (“white box” model), two “gray box” models, which integrate theory on turbomachines with specific data correlations, and one “black box” model. More in detail, the modeling approaches are: (1) a physics-based simulation model developed by the same authors, which includes the equations for estimating head, power, and efficiency and uses loss coefficients and specific parameters; (2) a model developed by Derakhshan and Nourbakhsh, which first predicts the best efficiency point of a PAT and then reconstructs their complete characteristic curves by means of two ad hoc equations; (3) the prediction model developed by Singh and Nestmann, which predicts the complete turbine characteristics based on pump shape and size; (4) an Evolutionary Polynomial Regression model, which represents a data-driven hybrid scheme which can be used for identifying the explicit mathematical relationship between PAT and pump curves. All approaches are applied to literature data, relying on both pump and PAT performance curves of head, power, and efficiency over the entire range of operation. The experimental data were provided by Derakhshan and Nourbakhsh for four different turbomachines, working in both pump and PAT mode with specific speed values in the range 1.53–5.82. This paper provides a quantitative assessment of the predictions made by means of the considered approaches and also analyzes consistency from a physical point of view. Advantages and drawbacks of each method are also analyzed and discussed.

Development of robust evolutionary polynomial regression network in the estimation of stable alluvial channel dimensions

Geomorphology ◽  
2020 ◽  
Vol 350 ◽  
pp. 106895 ◽  
Author(s):  
Hossein Bonakdari ◽  
Azadeh Gholami ◽  
Ahmed M.A. Sattar ◽  
Bahram Gharabaghi
Keyword(s):  
Polynomial Regression ◽  
Evolutionary Polynomial Regression ◽  
Alluvial Channel

Predicting Landslide Displacements by Multi-objective Evolutionary Polynomial Regression

2014 ◽  
pp. 651-654 ◽  
Author(s):  
Angelo Doglioni ◽  
Giovanni B. Crosta ◽  
Paolo Frattini ◽  
Nicola L. Melidoro ◽  
Vincenzo Simeone
Keyword(s):  
Polynomial Regression ◽  
Multi Objective ◽  
Evolutionary Polynomial Regression
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