Automated discrete element method calibration using genetic and optimization algorithms
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(2) EPJ Web of Conferences 140 , 15011 (2017 ). DOI: 10.1051/epjconf/201714015011. Powders & Grains 2017. Automated discrete element method calibration using genetic and optimization algorithms Huy Q. Do, Alejandro M. Aragón, and Dingena L. Schott* Faculty of Mechanical, Maritime and Materials Engineering, Delft University of Technology, Mekelweg 2, 2628CD Delft, The Netherlands. Abstract. This research aims at developing a universal methodology for automated calibration of microscopic properties of modelled granular materials. The proposed calibrator can be applied for different experimental set-ups. Two optimization approaches: (1) a genetic algorithm and (2) DIRECT optimization, are used to identify discrete element method input model parameters, e.g., coefficients of sliding and rolling friction. The algorithms are used to minimize the objective function characterized by the discrepancy between the experimental macroscopic properties and the associated numerical results. Two test cases highlight the robustness, stability, and reliability of the two algorithms used for automated discrete element method calibration with different set-ups.. 1 Introduction *B4 >5 C74 38B2A4C4 4;4<4=C <4C7>3 # 70B 144= A8B8=6F8C78=C74?>F34AA0F<0C4A80;0=31D;:<0C4A80; 70=3;8=6 8=3DBCAH 5>A 0=0;HI8=6 0=3 34B86=8=6 <0C4A80; 70=3;8=6 BHBC4<B >F4E4A 0 <09>A 10AA84A C> C74 45542C8E4 DB4 >5 # 5>A 8=3DBCA80; 0??;820C8>=B 8B B4;42C8=6 0??A>?A80C4 8=?DC ?0A0<4C4AB B> C70C B8<D;0C8>=B20=022DA0C4;HA4?A>3D24C741470E8>A>5A40; BHBC4<B 20;81A0C8>=0??A>0278B2><<>=;H27>B4=5>A C7434C4A<8=0C8>=>5C74B4?0A0<4C4ABC70C 0A4=>C40B8;H <40BDA43 8= 4G?4A8<4=C 0;81A0C8>= DB8=6 # B8<D;0C8>=B 8B 02CD0;;H 0= 8C4A0C8E4 ?A>24BB >5 039DBC8=6 8=?DC ?0A0<4C4AB BD27 C70C C74 <02A>B2>?82 A4BD;CB >5 B8<D;0C8>=B 0=3 4G?4A8<4=CB 0A4 4@D8E0;4=C )A80; 0=3 4AA>A 4<?8A820; ?A>243DA4B 0A4 E4AH C8<42>=BD<8=6 0=3D=?A02C820;?0AC82D;0A;H 5>A# <>34;B8=E>;E8=6 <0=H ?0A0<4C4AB 148=6 20;81A0C43 (><4 0CC4<?CB 70E4 144=A424=C;H<034C>8<?A>E4C7420;81A0C8>=?A>243DA4 4G?4A8<4=C0;34B86=.
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(24) EPJ Web of Conferences 140 , 15011 (2017 ). DOI: 10.1051/epjconf/201714015011. Powders & Grains 2017. Fig. 5. AoR in degree as a function of the rolling and sliding coefficients from the rectangular container simulations.. Fig. 2. AoR in degree as a function of the rolling and sliding coefficients from the hourglass simulations .
(25) /.. Fig. 6. Reference result defined by the intersection of simulated two contour-lines of hourglass discharging time and rectangular container AoR referred from the experimental results.. Fig. 3. Discharging time in second as a function of the rolling and sliding coefficients from the hourglass simulations .
(26) /. 4 Results and Discussion # B8<D;0C8>=B 0A4 C8<42>=BD<8=6 )> A43D24 2><?DC0C8>=0; C8<4 3DA8=6 C74 20;81A0C8>= ?A>24BB C74 6A83B>5GB0<?;4?>8=CB8= 86 0=30A4A4DB435>A 18;8=40A8=C4A?>;0C8=6E0;D4B>5 0=338B270A68=6C8<4 5>A0A18CA0AHMA0=3MB 4.1 Calibration using hourglass - first sample test case 4.1.1 Result from GA
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(46) AD=B0A420AA843>DCC0A64C8=6C74=4F58C=4BB 5D=2C8>= 5C4A 8C4A0C8>=B >5 0;; AD=B C74 2><?DC43 A>;;8=6 0=3 B;838=6 5A82C8>= 2>4558284=CB 0A4 L 0=3 L A4B?42C8E4;H 0=3 C748A 0BB>280C43 >?C8<0; 58C=4BB E0;D4 8B L B 4G?42C43 C74 A4BD;C 8B 8= 6>>3 06A44<4=C F8C7 C74 A454A4=24 A4BD;C 0B B7>F=8= 86 . 12. 13. 14.. 4.2.2 Result from DIRECT. 4. ->>= =C '>2: #427 #8= (28
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(65) 8 74= # '>CC4A - %>8 &>F34A )427=>;
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(67) N. Estrada, E. Azéma, F. Radjai, A. Taboada, Phys. Rev. E, 84(1), 011306 (2011) ( # 4A0:7B70=8 " (27>CC ">34F89:B &>F34A )427=>;
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