The aim of the proposed work is to study the optimal design of an innovative Thermo-Acoustic-Magneto-Hydro-Dynamic electric generator, with particular reference to the Magneto-Hydro-Dynamic section. A multi-objective optimization algorithm, which makes use of a Tabu Search meta-heuristic, has been developed to this purpose. Thermo-Acoustic and Magneto-Hydro-Dynamic energy conversion processes give a great advantage by converting energy without solid moving components. This makes the cited technologies very interesting for the low weight, the low maintenance costs, and the high conversion efficiency. The design of the generator has to be optimized by considering conflicting objectives, i.e., maximizing the output power, minimizing the applied electrical voltage, and minimizing mass and size of the device. Therefore, a multi-objective vectorial optimization approach is mandatory. A fully vector scheme has been implemented that takes under control both the Pareto optimality of the solutions, and the uniformity in the Pareto front sampling.
CoelhoL. and AlottoP., Multiobjective Electromagnetic Optimization Based on a Nondominated Sorting Genetic Approach With a Chaotic Crossover Operator, IEEE Transactions on Magnetics44(6) (2008), 1078–1081, doi: 10.1109/TMAG.2007.916027.
2.
CarcangiuS.FanniA. and MontisciA., Multiobjective Tabu Search Algorithms for Optimal Design of Electromagnetic Devices, IEEE Transactions on Magnetics44(6) (2008), 970–973, doi: 10.1109/TMAG.2007.916336.
3.
CoelhoL. and AlottoP., Gaussian Artificial Bee Colony Algorithm Approach Applied to Loney’s Solenoid Benchmark Problem, IEEE Transactions on Magnetics47(5) (2011), 1326–1329, doi: 10.1109/TMAG.2010.2087317.
4.
KhanS.U.YangS.WangL. and LiuL., A Modified Particle Swarm Optimization Algorithm for Global Optimizations of Inverse Problems, IEEE Transactions on Magnetics52(3) (2016), 1–4, doi: 10.1109/TMAG.2015.2487678.
5.
CarcangiuS.Di BarbaP.FanniA.MognaschiM.E. and MontisciA., Comparison of multi-objective optimisation approaches for inverse magnetostatic problems, COMPEL – The International Journal for Computation and Mathematics in Electrical and Electronic Engineering26(2) (2007), 293–305, doi: 10.1108/03321640710727656.
6.
YangS.Y.CardosoJ.R.HoS.L.NiP.H.MachadoJ.M. and LoE.W.C., An improved tabu based vector optimal algorithm for design optimizations of electromagnetic devices, IEEE Transactions on Magnetics40(2) (2014), 1140–1143, doi: 10.1109/TMAG.2004.824798.
7.
AlottoP. and NerviM.A., An efficient algorithm for the optimization of problems with several local minima, Int. J. Numer. Methods Eng.50 (2001), 847–868.
8.
CanovaA.GruossoG. and RepettoM., Magnetic design optimization and objective function approximation, IEEE Transactions on Magnetics39(5) (2003), 2154–2162, doi: 10.1109/TMAG.2003.817076.
9.
TakahashiN.MuramatsuK.NatsumedaM.OhashiK.MiyataK. and SayamaK., Solution of problem 25 (Optimization of die press model), In: Proc. ICEF’96, Hubei, China, 9–11 Oct. 1996, 383–386.
10.
CherubiniD.FanniA.MontisciA. and TestoniP., Inversion of MLP neural network for direct solution of inverse problems, IEEE Transactions on Magnetics41(5) (2005), 1784–1787, doi: 10.1109/TMAG.2005.845987.
11.
AlemanyA.CarcangiuS.ForcinettiR.MontisciA. and RouxJ.P., Feasibility analysis of a MHD inductive Generator coupled with a thermoacoustic resonator, Magnetohydrodynamics51(1) (2015), 1–8.
12.
BackhausS.TwardE. and PetachM., Thermoacoustic power systems for space applications, AIP Conference Proceedings608 (2002), 939–944, doi: 10.1063/1.1449822.
13.
BackhausS.TwardE. and PetachM., Traveling-wave thermos-acoustic electric generator, Applied Physics Letters85(6) (2004), 1085–1087, doi: 10.1063/1.1781739.
14.
MirhoseiniS.M.H. and AlemanyA., Analytical study of thermoacoustic MHD generator, Magnetohydrodynamics51(3) (2015), 519–530.
15.
WangK.SunD.M.ZhangJ. et al., Operating characteristics and performance improvements of a 500 W traveling-wave thermoacoustic electric generator, Applied Energy160 (2015), 853–862, doi: 10.1016/j.apenergy.2015.03.085.
16.
AlemanyA.KrauzeA. and RadiM.A., Thermo-acoustic-MHD electrical Generator, Energy Procedia6, (2011), 92–100, doi: 10.1016/j.egypro.2011.05.011.
17.
RosaR.J., Magnetohydrodynamic energy conversion, United States: Hemisphere Publishing, New York, NY, 1987.
MaiorovM.M.BlūmsE.KroņkalnsG.KrūmiņaA. and LubāneM., Magnetic nanoparticles from spark discharge, Magnetohydrodynamics52(4) (2016), 507–512.
20.
ProskurinA. and SagalakovA., A method for modelling MHD flows in ducts, Magnetohydrodynamics52(1/2) (2016), 163–170.
21.
MolevichN.E.RyashchikovD.S. and ZavershinskiyD.I., Influence of thermal conduction on MHD waves properties in thermally unstable plasma, Magnetohydrodynamics52(1/2) (2016), 199–208.
22.
RappD.SurampudiS.NesmithB. and MondtJ., Advanced Radioisotope Power System Technology Development for NASA Missions 2011 and Beyond, in: Proc. of 6th European Space Power Conf., A. Wilson, ed., European Space Agency, ESA SP-502, 2002, 291.
23.
CarcangiuS.ForcinettiR. and MontisciA., Multiphysics Analysis of a Thermo Acoustic MHD Inductive Generator, in: Proceedings of the European COMSOL Conference, Grenoble, France, 2015.
24.
Space TRIPS project. Space trips: The up to date situation. Available on line. http://space-trips.eu.seventhframework.sal.lv/wp-content/uploads/2015/06/Space-TRIPS-up-to-date.pdf.
25.
BullardG.L.Sierra-AlcazarH.B.LeeH.L. and MorrisJ.L., Operating principles of the ultracapacitor, IEEE Transactions on Magnetics25(1) (1989), 102–106, doi: 10.1109/20.22515.
26.
BiesheuvelP.M. and BazantM.Z., Nonlinear dynamics of capacitive charging and desalination by porous electrodes, Physical Review E81(3) (2010), 031502—031512, doi: 10.1103/PhysRevE.81.031502.
27.
DelgadoA.V.González-CaballeroF.HunterR.J.KoopalL.K. and LyklemaJ., Measurement and interpretation of electrokinetic phenomena, Journal of Colloid and Interface Science309(2) (2007), 194–224, doi: 10.1016/j.jcis.2006.12.075.
28.
ShokrollahiH., The magnetic and structural properties of the most important alloys of iron produced by mechanical alloying, Materials & Design30(9) (2009), 3374–3387, doi: 10.1016/j.matdes.2009.03.035.
