This study concerns numerical simulation, modeling and optimization of aerodynamic stall control using a synthetic jet actuator. The numerical simulation was carried out by a large-eddy simulation that employs a RNG-based model as the subgrid-scale model. The flow around a NACA0015 airfoil, including a synthetic jet located at 10 % of the chord, is studied under Reynolds number Re = 12.7 × 10 a...

The effective thermal conductivity (ETC) of soil is an essential parameter for the design and unhindered operation underground energy transportation storage systems. Various experimental, empirical, semi-empirical, mathematical, numerical methods have been tried in past, but lack either accuracy or are computationally cumbersome. recent developments computer science provided a new computational...

Residential sector is one of the energy-consuming districts countries that causes CO2 emission in large extent. In this regard, must be considered energy policy making related to reduction and other greenhouse gases. present work, residential three countries, including Indonesia, Thailand, Vietnam Southeast Asia, are discussed modeled by employing Group Method Data Handling (GMDH) Multilayer Pe...

The treatment of manufacturing problems, whether in process control, process optimization, or system design and planning, can be helped by input-output models, namely, relationships between input and output variables. Artificial neural networks present an opportunity to "learn" empirically established relationships and apply them subsequently in order to solve a particular problem. In light of ...

Proper performance of water distribution networks (WDNs) plays a vital role in customer satisfaction. The aim this study is to conduct sensitivity analysis evaluate the behavior WDNs analyzed by pressure-driven (PDA) approach and classification technique using an appropriate artificial neural network, namely Group Method Data Handling (GMDH). For purpose, divided into four distinct steps. In fi...

In this paper, we propose the design procedure of advanced Polynomial Neural Networks(PNN) architecture fo r optimal model identification of complex and nonlinear system. The proposed PNN architecture is presented as the generic and advanced type. The essence of the design procedure dwells on the Group Method of Data Handling (GMDH). PNN is a flexible neural architecture whose structure is deve...

Many studies have demonstrated that combining forecasts improves accuracy relative to individual forecasts. In this paper, the combing forecasts is used to improve on individual forecasts is investigated. A combining approach based on the modified Group Method Data Handling (GMDH) method and genetic algorithm (GA), is called as the GAGMDH model is proposed. Four time series forecasting techniqu...

This article deals with the problem of determination of the model uncertainty during the system identification via application of the self-organising group method of data handling (GMDH) neural network. In particular, the contribution of the neural network structure errors and the parameter estimates inaccuracy to the model uncertainty were presented. Knowing these sources and applying the Oute...