Design concepts of induction mass heating technology based on multiple-criteria optimization

Abstract

Purpose – The purpose of this paper is to describe main ideas and demonstrate results of the research\ud activities carried out by the authors in the field of design concepts of induction mass heating technology\ud based on multiple-criteria optimization. The main goal of the studies is the application of different\ud optimization methods and numerical finite element method (FEM) codes for field analysis to solve the\ud multi-objective optimization problem that is mathematically formulated in terms of the most important\ud optimization criteria, for example, maximum temperature uniformity, maximum energy efficiency and\ud minimum scale formation.\ud Design/methodology/approach – Standard genetic algorithm (GA), non-dominated sorting genetic\ud algorithm (NSGA) and alternance method of parametric optimization based on the optimal control theory are\ud applied as effective tools for the practice-oriented problems for multiple-criteria optimization of induction\ud heaters’ design based on non-linear coupled electromagnetic and temperature field analysis. Different\ud approaches are used for combining FEM codes for interconnected field analysis and optimization algorithms\ud into the automated optimization procedure.\ud Findings – Optimization procedures are tested and investigated for two- and three-criteria optimization\ud problems solution on the examples of induction heating of a graphite disk, induction heating of aluminum and\ud steel billets prior to hot forming.\ud Practical implications – Solved problems are based on the design of practical industrial applications.\ud The developed optimization procedures are planned to be applied to the wide range of real-life problems of the\ud optimal design and control of different electromagnetic devices and systems.\ud Originality/value – The paper describes main ideas and results of the research activities carried out by the\ud authors during past years in the field of multiple-criteria optimization of induction heaters’ design based on\ud numerical coupled electromagnetic and temperature field analysis. Implementing the automated procedure that combines a numerical FEM code for coupled field analysis with an optimization algorithm and its subsequent\ud application for designing induction heaters makes the proposed approach specific and original. The paper also\ud demonstrates that different optimization strategies used (standard GA, NSGA-II and the alternance method of\ud optimal control theory) are effective for real-life industrial applications for multiple-criteria optimization of\ud induction heaters design

A novel multi-surrogate multi-objective decision-making optimization algorithm in induction heating

“…Let us consider a cylindrical work-piece made of carbon steel with a diameter of 100 mm. Supposed the coil is long enough that edge effects are negligible, the trade-off between temperature homogeneity (16) and energy consumption (17) is investigated (Figure 12) (Pleshivtseva et al , 2017). The design vector has then two variables x ̄ = ( t heating , t soaking ), i.e.…”

“…Many induction heating problems have multiple conflicting objectives f 1 , f 2 ,…, f M that need to be minimized/maximized simultaneously (Sieni et al , 2018; Pleshivtseva et al , 2017). The goal of a multi-objective optimization problem is to determine the so-called “Pareto front”, i.e.…”

Design and control of electrotechnological systems

“…The function S ( x ̄, t , p ̄) depending on time t and vector of spatial coordinates Xtrue¯=true(xitrue), i=1, 3true¯ can represent the controlled function of the optimized process subject to the solution of the system of Maxwell’s equations of electromagnetic field, as well as Fourier’s equation of heat transfer, so a coupled-field – or multiphysics – problem is originated (Di Barba, 2014; Pleshivtseva et al , 2017a).…”

Optimal design methods for the uniform heating of tube ends for stress relieving

“…The paper shows some preliminary results that demonstrate how modern multi-objective optimization algorithms may help the designer of induction heating coils to find good design compromises when dealing with a big number of design variables (Di Barba et al , 2015, 2014; Di Barba et al , 2013; Di Barba et al , 2017c). In fact, modern multi-objective optimization algorithms such as genetic algorithms (Deb et al , 2002; Paolo Di Barba et al , 2018b, 2017a; Pleshivtseva et al , 2017), wind-driven optimization (Paolo Di Barba et al , 2018c), biogeography-inspired optimization (Simon, 2008; Paolo Di Barba et al , 2018a;Mognaschi, 2017; Paolo Di Barba et al , 2017b; Di Barba et al , 2016b) may help the designer of induction heating coils to find improved solutions. In particular, these methods have shown good performances when dealing with a large number of design variables (Di Barba et al , 2015).…”