Intelligent flow friction estimation

Nowadays, the Colebrook equation is used as a mostly accepted relation for the calculation of fluid flow friction factor. However, the Colebrook equation is implicit with respect to the friction factor (). In the present study, a noniterative approach using Artificial Neural Network (ANN) was developed to calculate the friction factor. To configure the ANN model, the input parameters of the Reynolds Number (Re) and the relative roughness of pipe () were transformed to logarithmic scales. The 90,000 sets of data were fed to the ANN model involving three layers: input, hidden, and output layers with, 2, 50, and 1 neurons, respectively. This configuration was capable of predicting the values of friction factor in the Colebrook equation for any given values of the Reynolds number (Re) and the relative roughness () ranging between 5000 and 108 and between 10−7 and 0.1, respectively. The proposed ANN demonstrates the relative error up to 0.07% which had the high accuracy compared with the vast majority of the precise explicit approximations of the Colebrook equation

GEOMETRICALLY NONLINEAR ANALYSIS OF PIEZOELECTRIC ACTIVE LAMINATED SHELLS BY MEANS OF ISOGEOMETRIC FE FORMULATION

The topic of piezoelectric active thin-walled structures has attracted a great deal of attention over the previous couple of decades. Lightweight structures with piezoelectric material based active elements, sensors and actuators, offer numerous advantages over their passive counterparts. This explains the motivation of authors to dedicate their work to this enticing research field. Accurate and reliable numerical tools for modeling and simulation of this type of structures is still a hot topic in the research community. This paper offers an isogeometric finite element formulation for shell type of structures made of composite laminates including piezoelectric layers characterized by the electro-mechanical coupling. The shell kinematics is based on the Mindlin-Reissner assumptions, thus including the transverse shear effects. A few examples selected from the available literature are considered to demonstrate the applicability of the developed numerical tool and assess its performance

Evolutionary optimization of Colebrook’s turbulent flow friction approximations

This paper presents evolutionary optimization of explicit approximations of the empirical Colebrook’s equation that is used for the calculation of the turbulent friction factor (λ), i.e., for the calculation of turbulent hydraulic resistance in hydraulically smooth and rough pipes including the transient zone between them. The empirical Colebrook’s equation relates the unknown flow friction factor (λ) with the known Reynolds number (R) and the known relative roughness of the inner pipe surface (ε/D). It is implicit in the unknown friction factor (λ). The implicit Colebrook’s equation cannot be rearranged to derive the friction factor (λ) directly, and therefore, it can be solved only iteratively [λ = f(λ, R, ε/D)] or using its explicit approximations [λ≈f(R, ε/D)], which introduce certain error compared with the iterative solution. The optimization of explicit approximations of Colebrook’s equation is performed with the aim to improve their accuracy, and the proposed optimization strategy is demonstrated on a large number of explicit approximations published up to date where numerical values of the parameters in various existing approximations are changed (optimized) using genetic algorithms to reduce maximal relative error. After that improvement, the computational burden stays unchanged while the accuracy of approximations increases in some of the cases very significantly

Fuzzy-neuro-genetic aerofin control

U ovom radu razmatrano je fazi-genetsko-neuro upravljanje elektromehaničkog aktuatora aerokrila za kontrolu leta projektila, pokretanim motorom jednosmerne struje sa četkicama i permanentnim magnetom koji je pogonjen drajverom sa konstantnom strujom. U našim prethodnim radovima, na osnovu razvijenog nelinearnog modela sistema, razvijena su i testirana različta konvencionalna i hibridna konvencionalno-inteligentna upravljanja. U ovom radu predloženo je fazi i neuro-fazi upravljanje sa genetskom optimizacijom. Predloženi inteligentni upravljački sistemi, koji obezbeđuju dobro ponašanje sistema, su verifikovani numeričkim simulacijima i upoređeni sa prethodnim rezultatima. .In this paper fuzzy-neuro-genetic control of an electromechanical actuator (EMA) system for aerofin control (AFC), with permanent magnet brush DC motor driven by a constant current driver, is investigated. In our previous papers, nonlinear model of the EMA-AFC system and different classical and hybrid classicalcomputationally intelligent control systems have been designed and tested. In this paper we have proposed fuzzy and neuro-fuzzy control with genetic optimization. Proposed intelligent control systems, providing good transient response and system behaviour, have been validated by various numerical experiments and compared to previous results.

THE VEHICLE ROUTING PROBLEM WITH STOCHASTIC DEMANDS IN AN URBAN AREA – A CASE STUDY

The vehicle routing problem with stochastic demands (VRPSD) is a combinatorial optimization problem. The VRPSD looks for vehicle routes to connect all customers with a depot, so that the total distance is minimized, each customer visited once by one vehicle, every route starts and ends at a depot, and the travelled distance and capacity of each vehicle are less than or equal to the given maximum value. Contrary to the classical VRP, in the VRPSD the demand in a node is known only after a vehicle arrives at the very node. This means that the vehicle routes are designed in uncertain conditions. This paper presents a heuristic and meta-heuristic approach for solving the VRPSD and discusses the real problem of municipal waste collection in the City of Niš

Enhanced control of radiator heating system

In this paper a radiator heating system of a building is considered. For the purpose of the heating system optimization, a mathematical model of the system is developed. The linear quadratic algorithm with integral action is proposed and analyzed. This solution has proven to be expensive. Further analysis of the model is done and a reduction of the order of the system is proposed. An inverse-based controller design approach for minimum phase first order system is used to provide realizable controller in the form of proportional integral controller. Optimal parameters of the control algorithm parameters have been chosen by integral of time absolute error criterion, and also by metaheuristic optimization. According to the real heating demand, a simulation of the plant is performed Proposed controllers were tested by numerical simulation for a typical winter day for geographical region of the building. It is shown that advanced performance can be achieved with optimized control systems, and that by controller optimization a significant reduction of the energy consumption is obtained without losing the indoor comfort. This has also proved to be more economical solution

COMPARISON OF THREE FUZZY MCDM METHODS FOR SOLVING THE SUPPLIER SELECTION PROBLEM

The evaluation and selection of an optimal, efficient and reliable supplier is becoming more and more important for companies in today’s logistics and supply chain management. Decision-making in the supplier selection domain, as an essential component of the supply chain management, is a complex process since a wide range of diverse criteria, stakeholders and possible solutions are embedded into this process. This paper shows a fuzzy approach in multi – criteria decision-making (MCDM) process. Criteria weights have been determined by fuzzy SWARA (Step-wise Weight Assessment Ratio Analysis) method. Chosen methods, fuzzy TOPSIS (Technique for the Order Preference by Similarity to Ideal Solution), fuzzy WASPAS (Weighted Aggregated Sum Product Assessment) and fuzzy ARAS (Additive Ratio Assessment) have been used for evaluation and selection of suppliers in the case of procurement of THK Linear motion guide components by the group of specialists in the “Lagerton” company in Serbia. Finally, results obtained using different MCDM approaches were compared in order to help managers to identify appropriate method for supplier selection problem solving

Temperature controller optimization by computational intelligence

In this paper a temperature control system for an automated educational classroom is optimized with several advanced computationally intelligent methods. Controller development and optimization has been based on developed and extensively tested mathematical and simulation model of the observed object. For the observed object cascade P-PI temperature controller has been designed and conventionally tuned. To improve performance and energy efficiency of the system, several meta heuristic optimizations of the controller have been attempted, namely genetic algorithm optimization, simulated annealing optimization, particle swarm optimization and ant colony optimization. Efficiency of the best results obtained with proposed computationally intelligent optimization methods has been compared with conventional controller tuning. Results presented in this paper demonstrate that heuristic optimization of advanced temperature controller can provide improved energy efficiency along with other performance improvements and improvements regarding equipment wear. Not only that presented methodology provides for determination and tuning of the core controller, but it also allows that advanced control concepts such as anti-windup controller gain are optimized simultaneously, which is of significant importance since interrelation of all control system parameters has important influence on the stability and performance of the system as a whole. Based on the results obtained, general conclusions are presented indicating that meta heuristic computationally intelligent optimization of heating, ventilation, and air conditioning control systems is a feasible concept with strong potential in providing improved performance, comfort and energy efficiency

Temperature controller optimization by computational intelligence

In this paper a temperature control system for an automated educational classroom is optimized with several advanced computationally intelligent methods. Controller development and optimization has been based on developed and extensively tested mathematical and simulation model of the observed object. For the observed object cascade P-PI temperature controller has been designed and conventionally tuned. To improve performance and energy efficiency of the system, several meta heuristic optimizations of the controller have been attempted, namely genetic algorithm optimization, simulated annealing optimization, particle swarm optimization and ant colony optimization. Efficiency of the best results obtained with proposed computationally intelligent optimization methods has been compared with conventional controller tuning. Results presented in this paper demonstrate that heuristic optimization of advanced temperature controller can provide improved energy efficiency along with other performance improvements and improvements regarding equipment wear. Not only that presented methodology provides for determination and tuning of the core controller, but it also allows that advanced control concepts such as anti-windup controller gain are optimized simultaneously, which is of significant importance since interrelation of all control system parameters has important influence on the stability and performance of the system as a whole. Based on the results obtained, general conclusions are presented indicating that meta heuristic computationally intelligent optimization of heating, ventilation, and air conditioning control systems is a feasible concept with strong potential in providing improved performance, comfort and energy efficiency

Machine Learning Classification of Cervical Tissue Liquid Based Cytology Smear Images by Optomagnetic Imaging Spectroscopy

Semi-automated system for classification of cervical smear images based on Optomagnetic Imaging Spectroscopy (OMIS) and machine learning is proposed. Optomagnetic Imaging Spectroscopy has been applied to screen 700 cervical samples prepared according to Liquid Based Cytology (LBC) principles and to record spectra of the samples. Peak intensities and peak shift frequencies from the spectra have been used as features in classification models. Several machine learning algorithms have been tested and results of classification have been compared. Results suggest that the presented approach can be used to improve standard LBC screening tests for cervical cancer detection. Developed system enables detection of pre-cancerous and cancerous states with sensitivity of 79% and specificity of 83% along with AUC (ROC) of 88% and could be used as an improved alternative procedure for cervical cancer screening. Moreover, this can be achieved via portable apparatus and with immediately available results