Default is to use a total of 4 processors: 4 via shared-memory 1 via Linda Entering Link 1 = C:\G09W\l1.exe PID= 616. Copyright (c) 1988,1990,1992,1993,1995,1998,2003,2009,2013, Gaussian, Inc. All Rights Reserved. This is part of the Gaussian(R) 09 program. It is based on the Gaussian(R) 03 system (copyright 2003, Gaussian, Inc.), the Gaussian(R) 98 system (copyright 1998, Gaussian, Inc.), the Gaussian(R) 94 system (copyright 1995, Gaussian, Inc.), the Gaussian 92(TM) system (copyright 1992, Gaussian, Inc.), the Gaussian 90(TM) system (copyright 1990, Gaussian, Inc.), the Gaussian 88(TM) system (copyright 1988, Gaussian, Inc.), the Gaussian 86(TM) system (copyright 1986, Carnegie Mellon University), and the Gaussian 82(TM) system (copyright 1983, Carnegie Mellon University). Gaussian is a federally registered trademark of Gaussian, Inc. This software contains proprietary and confidential information, including trade secrets, belonging to Gaussian, Inc. This software is provided under written license and may be used, copied, transmitted, or stored only in accord with that written license. The following legend is applicable only to US Government contracts under FAR: RESTRICTED RIGHTS LEGEND Use, reproduction and disclosure by the US Government is subject to restrictions as set forth in subparagraphs (a) and (c) of the Commercial Computer Software - Restricted Rights clause in FAR 52.227-19. Gaussian, Inc. 340 Quinnipiac St., Bldg. 40, Wallingford CT 06492 --------------------------------------------------------------- Warning -- This program may not be used in any manner that competes with the business of Gaussian, Inc. or will provide assistance to any competitor of Gaussian, Inc. The licensee of this program is prohibited from giving any competitor of Gaussian, Inc. access to this program. By using this program, the user acknowledges that Gaussian, Inc. is engaged in the business of creating and licensing software in the field of computational chemistry and represents and warrants to the licensee that it is not a competitor of Gaussian, Inc. and that it will not use this program in any manner prohibited above. --------------------------------------------------------------- Cite this work as: Gaussian 09, Revision D.01, M. J. Frisch, G. W. Trucks, H. B. Schlegel, G. E. Scuseria, M. A. Robb, J. R. Cheeseman, G. Scalmani, V. Barone, B. Mennucci, G. A. Petersson, H. Nakatsuji, M. Caricato, X. Li, H. P. Hratchian, A. F. Izmaylov, J. Bloino, G. Zheng, J. L. Sonnenberg, M. Hada, M. Ehara, K. Toyota, R. Fukuda, J. Hasegawa, M. Ishida, T. Nakajima, Y. Honda, O. Kitao, H. Nakai, T. Vreven, J. A. Montgomery, Jr., J. E. Peralta, F. Ogliaro, M. Bearpark, J. J. Heyd, E. Brothers, K. N. Kudin, V. N. Staroverov, T. Keith, R. Kobayashi, J. Normand, K. Raghavachari, A. Rendell, J. C. Burant, S. S. Iyengar, J. Tomasi, M. Cossi, N. Rega, J. M. Millam, M. Klene, J. E. Knox, J. B. Cross, V. Bakken, C. Adamo, J. Jaramillo, R. Gomperts, R. E. Stratmann, O. Yazyev, A. J. Austin, R. Cammi, C. Pomelli, J. W. Ochterski, R. L. Martin, K. Morokuma, V. G. Zakrzewski, G. A. Voth, P. Salvador, J. J. Dannenberg, S. Dapprich, A. D. Daniels, O. Farkas, J. B. Foresman, J. V. Ortiz, J. Cioslowski, and D. J. Fox, Gaussian, Inc., Wallingford CT, 2013. ****************************************** Gaussian 09: EM64W-G09RevD.01 13-Apr-2013 13-Oct-2015 ****************************************** %chk=L:\GaussView\Chair and Boat TS\boat_ts_qst2.chk Default route: MaxDisk=10GB ------------------------------------------------------------------ # opt=qst2 freq hf/3-21g geom=connectivity integral=grid=ultrafine ------------------------------------------------------------------ 1/5=1,18=20,27=202,38=1,57=2/1,3; 2/9=110,12=2,17=6,18=5,40=1/2; 3/5=5,11=9,16=1,25=1,30=1,71=1,75=-5/1,2,3; 4//1; 5/5=2,38=5/2; 6/7=2,8=2,9=2,10=2,28=1/1; 7//1,2,3,16; 1/5=1,18=20,27=202/3(2); 2/9=110/2; 99//99; 2/9=110/2; 3/5=5,11=9,16=1,25=1,30=1,71=1,75=-5/1,2,3; 4/5=5,16=3,69=1/1; 5/5=2,38=5/2; 7//1,2,3,16; 1/5=1,18=20,27=202/3(-5); 2/9=110/2; 6/7=2,8=2,9=2,10=2,19=2,28=1/1; 99/9=1/99; ------------------- Title Card Required ------------------- Symbolic Z-matrix: Charge = 0 Multiplicity = 1 C -2.95642 -0.21892 0.1464 C -1.87024 0.45409 -0.16908 C -0.54395 -0.16993 -0.52724 C 0.54397 0.17004 0.52733 C 1.87026 -0.45407 0.16925 C 2.95638 0.21881 -0.1467 H 2.97507 1.29326 -0.15474 H 3.87302 -0.27492 -0.40773 H 1.89032 -1.53081 0.16633 H 0.21024 -0.19685 1.49298 H 0.64956 1.24707 0.60198 H -0.64949 -1.24697 -0.60192 H -0.21027 0.19699 -1.49288 H -1.89016 1.53083 -0.16514 H -2.9752 -1.29338 0.15369 H -3.87306 0.2747 0.40767 ------------------- Title Card Required ------------------- Symbolic Z-matrix: Charge = 0 Multiplicity = 1 C 0.54397 0.17004 0.52733 C 1.87026 -0.45407 0.16925 C 2.95638 0.21881 -0.1467 C -2.95642 -0.21892 0.1464 C -1.87024 0.45409 -0.16908 C -0.54395 -0.16993 -0.52724 H 2.97507 1.29326 -0.15474 H 3.87302 -0.27492 -0.40773 H 1.89032 -1.53081 0.16633 H 0.21024 -0.19685 1.49298 H 0.64956 1.24707 0.60198 H -0.64949 -1.24697 -0.60192 H -0.21027 0.19699 -1.49288 H -1.89016 1.53083 -0.16514 H -2.9752 -1.29338 0.15369 H -3.87306 0.2747 0.40767 Iteration 1 RMS(Cart)= 0.09607760 RMS(Int)= 1.41401243 Iteration 2 RMS(Cart)= 0.08129288 RMS(Int)= 1.35453828 Iteration 3 RMS(Cart)= 0.08038532 RMS(Int)= 1.29930707 Iteration 4 RMS(Cart)= 0.08410695 RMS(Int)= 1.24656506 Iteration 5 RMS(Cart)= 0.08759757 RMS(Int)= 1.19637338 Iteration 6 RMS(Cart)= 0.08981905 RMS(Int)= 1.14917972 Iteration 7 RMS(Cart)= 0.08915891 RMS(Int)= 1.10595054 Iteration 8 RMS(Cart)= 0.08559824 RMS(Int)= 1.06782222 Iteration 9 RMS(Cart)= 0.08244478 RMS(Int)= 1.03485387 Iteration 10 RMS(Cart)= 0.07632966 RMS(Int)= 1.00720341 Iteration 11 RMS(Cart)= 0.07039846 RMS(Int)= 0.98481608 Iteration 12 RMS(Cart)= 0.06372751 RMS(Int)= 0.96747628 Iteration 13 RMS(Cart)= 0.05751198 RMS(Int)= 0.95427166 Iteration 14 RMS(Cart)= 0.05328031 RMS(Int)= 0.94435103 Iteration 15 RMS(Cart)= 0.04981889 RMS(Int)= 0.93680240 Iteration 16 RMS(Cart)= 0.04975605 RMS(Int)= 0.93102567 Iteration 17 RMS(Cart)= 0.05058468 RMS(Int)= 0.92677089 Iteration 18 RMS(Cart)= 0.05154543 RMS(Int)= 0.92303713 Iteration 19 RMS(Cart)= 0.05387712 RMS(Int)= 0.91933401 Iteration 20 RMS(Cart)= 0.05866320 RMS(Int)= 0.91584713 Iteration 21 RMS(Cart)= 0.06224175 RMS(Int)= 0.91300381 Iteration 22 RMS(Cart)= 0.06337668 RMS(Int)= 0.91139496 Iteration 23 RMS(Cart)= 0.05867486 RMS(Int)= 0.91131322 Iteration 24 RMS(Cart)= 0.04966699 RMS(Int)= 0.90770441 Iteration 25 RMS(Cart)= 0.05217574 RMS(Int)= 0.89788589 Iteration 26 RMS(Cart)= 0.04393287 RMS(Int)= 0.89174539 Iteration 27 RMS(Cart)= 0.03761235 RMS(Int)= 0.88746082 Iteration 28 RMS(Cart)= 0.02602026 RMS(Int)= 0.88407823 Iteration 29 RMS(Cart)= 0.03127855 RMS(Int)= 0.87596170 Iteration 30 RMS(Cart)= 0.03155943 RMS(Int)= 0.86617911 Iteration 31 RMS(Cart)= 0.02441800 RMS(Int)= 0.85845613 Iteration 32 RMS(Cart)= 0.02080081 RMS(Int)= 0.85216152 Iteration 33 RMS(Cart)= 0.01893702 RMS(Int)= 0.84702361 Iteration 34 RMS(Cart)= 0.01826387 RMS(Int)= 0.84258446 Iteration 35 RMS(Cart)= 0.01778631 RMS(Int)= 0.83863498 Iteration 36 RMS(Cart)= 0.01583995 RMS(Int)= 0.83499549 Iteration 37 RMS(Cart)= 0.01455737 RMS(Int)= 0.83164753 Iteration 38 RMS(Cart)= 0.01362692 RMS(Int)= 0.82856923 Iteration 39 RMS(Cart)= 0.01312119 RMS(Int)= 0.82564333 Iteration 40 RMS(Cart)= 0.01279232 RMS(Int)= 0.82287241 Iteration 41 RMS(Cart)= 0.01255219 RMS(Int)= 0.82025653 Iteration 42 RMS(Cart)= 0.01238766 RMS(Int)= 0.81779459 Iteration 43 RMS(Cart)= 0.01229723 RMS(Int)= 0.81548388 Iteration 44 RMS(Cart)= 0.01227976 RMS(Int)= 0.81332058 Iteration 45 RMS(Cart)= 0.01224810 RMS(Int)= 0.81130927 Iteration 46 RMS(Cart)= 0.01223149 RMS(Int)= 0.80945228 Iteration 47 RMS(Cart)= 0.01242708 RMS(Int)= 0.80772537 Iteration 48 RMS(Cart)= 0.01288993 RMS(Int)= 0.80610549 Iteration 49 RMS(Cart)= 0.01354354 RMS(Int)= 0.80458011 Iteration 50 RMS(Cart)= 0.01395251 RMS(Int)= 0.80316201 Iteration 51 RMS(Cart)= 0.01403613 RMS(Int)= 0.80188601 Iteration 52 RMS(Cart)= 0.01405574 RMS(Int)= 0.80076098 Iteration 53 RMS(Cart)= 0.01393191 RMS(Int)= 0.79979023 Iteration 54 RMS(Cart)= 0.01230671 RMS(Int)= 0.79892120 Iteration 55 RMS(Cart)= 0.01097313 RMS(Int)= 0.79818554 Iteration 56 RMS(Cart)= 0.00985843 RMS(Int)= 0.79759380 Iteration 57 RMS(Cart)= 0.00873973 RMS(Int)= 0.79713096 Iteration 58 RMS(Cart)= 0.00757606 RMS(Int)= 0.79677763 Iteration 59 RMS(Cart)= 0.00662236 RMS(Int)= 0.79650920 Iteration 60 RMS(Cart)= 0.00581951 RMS(Int)= 0.79630953 Iteration 61 RMS(Cart)= 0.00514201 RMS(Int)= 0.79616549 Iteration 62 RMS(Cart)= 0.00456975 RMS(Int)= 0.79606629 Iteration 63 RMS(Cart)= 0.00407310 RMS(Int)= 0.79600194 Iteration 64 RMS(Cart)= 0.00359204 RMS(Int)= 0.79594969 Iteration 65 RMS(Cart)= 0.00317922 RMS(Int)= 0.79588624 Iteration 66 RMS(Cart)= 0.00284863 RMS(Int)= 0.79582099 Iteration 67 RMS(Cart)= 0.00255519 RMS(Int)= 0.79575811 Iteration 68 RMS(Cart)= 0.00228659 RMS(Int)= 0.79570072 Iteration 69 RMS(Cart)= 0.00205341 RMS(Int)= 0.79564886 Iteration 70 RMS(Cart)= 0.00184930 RMS(Int)= 0.79560231 Iteration 71 RMS(Cart)= 0.00166980 RMS(Int)= 0.79556078 Iteration 72 RMS(Cart)= 0.00151157 RMS(Int)= 0.79552387 Iteration 73 RMS(Cart)= 0.00137179 RMS(Int)= 0.79549117 Iteration 74 RMS(Cart)= 0.00124803 RMS(Int)= 0.79546228 Iteration 75 RMS(Cart)= 0.00110469 RMS(Int)= 0.79543993 Iteration 76 RMS(Cart)= 0.00093968 RMS(Int)= 0.79542845 Iteration 77 RMS(Cart)= 0.00081045 RMS(Int)= 0.79542275 Iteration 78 RMS(Cart)= 0.00070629 RMS(Int)= 0.79542055 Iteration 79 RMS(Cart)= 0.00061954 RMS(Int)= 0.79542069 Iteration 80 RMS(Cart)= 0.00054598 RMS(Int)= 0.79542247 Iteration 81 RMS(Cart)= 0.00048294 RMS(Int)= 0.79542543 Iteration 82 RMS(Cart)= 0.00042855 RMS(Int)= 0.79542920 Iteration 83 RMS(Cart)= 0.00038139 RMS(Int)= 0.79543351 Iteration 84 RMS(Cart)= 0.00034032 RMS(Int)= 0.79543817 Iteration 85 RMS(Cart)= 0.00030442 RMS(Int)= 0.79544302 Iteration 86 RMS(Cart)= 0.00027293 RMS(Int)= 0.79544794 Iteration 87 RMS(Cart)= 0.00024522 RMS(Int)= 0.79545285 Iteration 88 RMS(Cart)= 0.00022075 RMS(Int)= 0.79545767 Iteration 89 RMS(Cart)= 0.00019908 RMS(Int)= 0.79546235 Iteration 90 RMS(Cart)= 0.00017985 RMS(Int)= 0.79546687 Iteration 91 RMS(Cart)= 0.00016272 RMS(Int)= 0.79547120 Iteration 92 RMS(Cart)= 0.00014744 RMS(Int)= 0.79547532 Iteration 93 RMS(Cart)= 0.00013377 RMS(Int)= 0.79547923 Iteration 94 RMS(Cart)= 0.00012152 RMS(Int)= 0.79548292 Iteration 95 RMS(Cart)= 0.00011052 RMS(Int)= 0.79548640 Iteration 96 RMS(Cart)= 0.00010062 RMS(Int)= 0.79548966 Iteration 97 RMS(Cart)= 0.00009170 RMS(Int)= 0.79549273 Iteration 98 RMS(Cart)= 0.00008365 RMS(Int)= 0.79549559 Iteration 99 RMS(Cart)= 0.00007637 RMS(Int)= 0.79549827 Iteration100 RMS(Cart)= 0.00006978 RMS(Int)= 0.79550076 New curvilinear step not converged. FormGI is forming the generalized inverse of G from B-inverse, IUseBI=4. RedQX1 iteration 1 Try 1 RMS(Cart)= 0.20162234 RMS(Int)= 1.32243974 XScale= 6.78028159 RedQX1 iteration 1 Try 2 RMS(Cart)= 0.20219510 RMS(Int)= 1.19331497 XScale= 3.47336625 RedQX1 iteration 1 Try 3 RMS(Cart)= 0.20496635 RMS(Int)= 1.09910890 XScale= 2.34614147 RedQX1 iteration 1 Try 4 RMS(Cart)= 0.21530519 RMS(Int)= 1.04598345 XScale= 1.75577701 RedQX1 iteration 1 Try 5 RMS(Cart)= 0.26917779 RMS(Int)= 1.04629411 XScale= 1.35240839 RedQX1 iteration 1 Try 6 RMS(Cart)= 0.17766663 RMS(Int)= 1.04778840 XScale= 1.25343815 RedQX1 iteration 2 Try 1 RMS(Cart)= 0.02199617 RMS(Int)= 1.04719119 XScale= 1.24805642 RedQX1 iteration 2 Try 2 RMS(Cart)= 0.02488708 RMS(Int)= 1.04674988 XScale= 1.24235724 RedQX1 iteration 2 Try 3 RMS(Cart)= 0.02943768 RMS(Int)= 1.04655444 XScale= 1.23629232 RedQX1 iteration 2 Try 4 RMS(Cart)= 0.03800225 RMS(Int)= 1.04695514 XScale= 1.22975451 RedQX1 iteration 2 Try 5 RMS(Cart)= 0.06246991 RMS(Int)= 1.05128895 XScale= 1.22125097 RedQX1 iteration 2 Try 6 RMS(Cart)= 0.05376643 RMS(Int)= 1.08034609 XScale= 1.20311642 RedQX1 iteration 3 Try 1 RMS(Cart)= 0.01882605 RMS(Int)= 1.07470650 XScale= 1.19627823 RedQX1 iteration 3 Try 2 RMS(Cart)= 0.07021048 RMS(Int)= 1.04436746 XScale= 1.23184153 RedQX1 iteration 3 Try 3 RMS(Cart)= 0.01866117 RMS(Int)= 1.04726877 XScale= 1.23248554 RedQX1 iteration 3 Try 4 RMS(Cart)= 0.02721408 RMS(Int)= 1.05933848 XScale= 1.22650124 RedQX1 iteration 3 Try 5 RMS(Cart)= 0.06836601 RMS(Int)= 1.05308804 XScale= 1.22445914 RedQX1 iteration 3 Try 6 RMS(Cart)= 0.26334937 RMS(Int)= 1.03647921 XScale= 1.22808528 RedQX1 iteration 4 Try 1 RMS(Cart)= 0.02678725 RMS(Int)= 1.03443561 XScale= 1.23201353 RedQX1 iteration 4 Try 2 RMS(Cart)= 0.02816272 RMS(Int)= 1.03371455 XScale= 1.23576178 RedQX1 iteration 4 Try 3 RMS(Cart)= 0.03099089 RMS(Int)= 1.03441964 XScale= 1.23951604 RedQX1 iteration 4 Try 4 RMS(Cart)= 0.03768113 RMS(Int)= 1.03658612 XScale= 1.24367595 RedQX1 iteration 4 Try 5 RMS(Cart)= 0.05881573 RMS(Int)= 1.04081084 XScale= 1.24870467 RedQX1 iteration 4 Try 6 RMS(Cart)= 0.04110594 RMS(Int)= 1.04337639 XScale= 1.24967278 RedQX1 iteration 5 Try 1 RMS(Cart)= 0.00770304 RMS(Int)= 1.04556159 XScale= 1.24787028 RedQX1 iteration 5 Try 2 RMS(Cart)= 0.01048319 RMS(Int)= 1.05155989 XScale= 1.24278997 RedQX1 iteration 5 Try 3 RMS(Cart)= 0.01729625 RMS(Int)= 1.08316392 XScale= 1.21613097 RedQX1 iteration 5 Try 4 RMS(Cart)= 0.05057706 RMS(Int)= 1.04785826 XScale= 1.23372915 RedQX1 iteration 5 Try 5 RMS(Cart)= 0.24436491 RMS(Int)= 1.03739006 XScale= 1.23096354 RedQX1 iteration 5 Try 6 RMS(Cart)= 0.12356685 RMS(Int)= 1.04011227 XScale= 1.23531394 RedQX1 iteration 6 Try 1 RMS(Cart)= 0.01375323 RMS(Int)= 1.04020290 XScale= 1.23798686 RedQX1 iteration 6 Try 2 RMS(Cart)= 0.01540820 RMS(Int)= 1.04064617 XScale= 1.24051210 RedQX1 iteration 6 Try 3 RMS(Cart)= 0.01816024 RMS(Int)= 1.04154614 XScale= 1.24282869 RedQX1 iteration 6 Try 4 RMS(Cart)= 0.02382520 RMS(Int)= 1.04341763 XScale= 1.24456704 RedQX1 iteration 6 Try 5 RMS(Cart)= 0.04265024 RMS(Int)= 1.05718106 XScale= 1.23669608 RedQX1 iteration 6 Try 6 RMS(Cart)= 0.06088352 RMS(Int)= 1.04893967 XScale= 1.23214455 RedQX1 iteration 7 Try 1 RMS(Cart)= 0.04910611 RMS(Int)= 1.06859241 XScale= 1.22524864 RedQX1 iteration 7 Try 2 RMS(Cart)= 0.02085663 RMS(Int)= 1.07459889 XScale= 1.20611625 RedQX1 iteration 7 Try 3 RMS(Cart)= 0.09447630 RMS(Int)= 1.03765888 XScale= 1.24443542 RedQX1 iteration 7 Try 4 RMS(Cart)= 0.02986713 RMS(Int)= 1.04164413 XScale= 1.24195915 RedQX1 iteration 7 Try 5 RMS(Cart)= 0.04784959 RMS(Int)= 1.06700214 XScale= 1.22276038 RedQX1 iteration 7 Try 6 RMS(Cart)= 0.07447108 RMS(Int)= 1.05221887 XScale= 1.22333431 RedQX1 iteration 8 Try 1 RMS(Cart)= 0.05233406 RMS(Int)= 1.09298749 XScale= 1.20028852 RedQX1 iteration 8 Try 2 RMS(Cart)= 0.02778455 RMS(Int)= 1.06249243 XScale= 1.21238264 RedQX1 iteration 8 Try 3 RMS(Cart)= 0.09133249 RMS(Int)= 1.04756843 XScale= 1.23300640 RedQX1 iteration 8 Try 4 RMS(Cart)= 0.03298931 RMS(Int)= 1.05890193 XScale= 1.22487711 RedQX1 iteration 8 Try 5 RMS(Cart)= 0.06762643 RMS(Int)= 1.06079053 XScale= 1.21295806 RedQX1 iteration 8 Try 6 RMS(Cart)= 0.27759327 RMS(Int)= 1.03907754 XScale= 1.21957264 RedQX1 iteration 9 Try 1 RMS(Cart)= 0.02859647 RMS(Int)= 1.03643855 XScale= 1.22458667 RedQX1 iteration 9 Try 2 RMS(Cart)= 0.03012001 RMS(Int)= 1.03511269 XScale= 1.22970113 RedQX1 iteration 9 Try 3 RMS(Cart)= 0.03315901 RMS(Int)= 1.03520998 XScale= 1.23521367 RedQX1 iteration 9 Try 4 RMS(Cart)= 0.04029972 RMS(Int)= 1.03661787 XScale= 1.24185260 RedQX1 iteration 9 Try 5 RMS(Cart)= 0.06273354 RMS(Int)= 1.03903285 XScale= 1.25133468 RedQX1 iteration 9 Try 6 RMS(Cart)= 0.04218427 RMS(Int)= 1.03816688 XScale= 1.25625206 RedQX1 iteration 10 Try 1 RMS(Cart)= 0.00680294 RMS(Int)= 1.03855992 XScale= 1.25586250 RedQX1 iteration 10 Try 2 RMS(Cart)= 0.00853973 RMS(Int)= 1.03962937 XScale= 1.25476263 RedQX1 iteration 10 Try 3 RMS(Cart)= 0.01176813 RMS(Int)= 1.04279405 XScale= 1.25165156 RedQX1 iteration 10 Try 4 RMS(Cart)= 0.01990695 RMS(Int)= 1.06061938 XScale= 1.23569056 RedQX1 iteration 10 Try 5 RMS(Cart)= 0.06891245 RMS(Int)= 1.04786220 XScale= 1.23199108 RedQX1 iteration 10 Try 6 RMS(Cart)= 0.24845677 RMS(Int)= 1.03853896 XScale= 1.22706427 RedQX1 iteration 11 Try 1 RMS(Cart)= 0.02548061 RMS(Int)= 1.03662614 XScale= 1.23016860 RedQX1 iteration 11 Try 2 RMS(Cart)= 0.02662355 RMS(Int)= 1.03599200 XScale= 1.23301383 RedQX1 iteration 11 Try 3 RMS(Cart)= 0.02908853 RMS(Int)= 1.03673395 XScale= 1.23579526 RedQX1 iteration 11 Try 4 RMS(Cart)= 0.03514423 RMS(Int)= 1.03900640 XScale= 1.23888833 RedQX1 iteration 11 Try 5 RMS(Cart)= 0.05526531 RMS(Int)= 1.04489368 XScale= 1.24201790 RedQX1 iteration 11 Try 6 RMS(Cart)= 0.04458364 RMS(Int)= 1.06356061 XScale= 1.23083980 RedQX1 iteration 12 Try 1 RMS(Cart)= 0.01421860 RMS(Int)= 1.09275432 XScale= 1.20689799 RedQX1 iteration 12 Try 2 RMS(Cart)= 0.02830383 RMS(Int)= 1.05441747 XScale= 1.22729792 RedQX1 iteration 12 Try 3 RMS(Cart)= 0.08397161 RMS(Int)= 1.04167252 XScale= 1.24578613 RedQX1 iteration 12 Try 4 RMS(Cart)= 0.02485349 RMS(Int)= 1.04880865 XScale= 1.24028256 RedQX1 iteration 12 Try 5 RMS(Cart)= 0.05239539 RMS(Int)= 1.06863497 XScale= 1.21069576 RedQX1 iteration 12 Try 6 RMS(Cart)= 0.27949658 RMS(Int)= 1.03897338 XScale= 1.21972395 RedQX1 iteration 13 Try 1 RMS(Cart)= 0.02947629 RMS(Int)= 1.03615221 XScale= 1.22470994 RedQX1 iteration 13 Try 2 RMS(Cart)= 0.03108218 RMS(Int)= 1.03463746 XScale= 1.22983289 RedQX1 iteration 13 Try 3 RMS(Cart)= 0.03422402 RMS(Int)= 1.03450796 XScale= 1.23544035 RedQX1 iteration 13 Try 4 RMS(Cart)= 0.04147864 RMS(Int)= 1.03562663 XScale= 1.24234082 RedQX1 iteration 13 Try 5 RMS(Cart)= 0.06417920 RMS(Int)= 1.03737439 XScale= 1.25268050 RedQX1 iteration 13 Try 6 RMS(Cart)= 0.04223886 RMS(Int)= 1.03524275 XScale= 1.25914663 RedQX1 iteration 14 Try 1 RMS(Cart)= 0.00650504 RMS(Int)= 1.03496327 XScale= 1.25943848 RedQX1 iteration 14 Try 2 RMS(Cart)= 0.00794078 RMS(Int)= 1.03483318 XScale= 1.25947501 RedQX1 iteration 14 Try 3 RMS(Cart)= 0.01037420 RMS(Int)= 1.03509194 XScale= 1.25894011 RedQX1 iteration 14 Try 4 RMS(Cart)= 0.01543121 RMS(Int)= 1.03665007 XScale= 1.25677685 RedQX1 iteration 14 Try 5 RMS(Cart)= 0.03218708 RMS(Int)= 1.04927331 XScale= 1.24361964 RedQX1 iteration 14 Try 6 RMS(Cart)= 0.05120913 RMS(Int)= 1.05956897 XScale= 1.21921871 RedQX1 iteration 15 Try 1 RMS(Cart)= 0.05238131 RMS(Int)= 1.05238932 XScale= 1.23486201 RedQX1 iteration 15 Try 2 RMS(Cart)= 0.01494110 RMS(Int)= 1.06427394 XScale= 1.22464004 RedQX1 iteration 15 Try 3 RMS(Cart)= 0.02486220 RMS(Int)= 1.12344445 XScale= 1.20025809 RedQX1 iteration 15 Try 4 RMS(Cart)= 0.04634937 RMS(Int)= 1.04857798 XScale= 1.23465384 RedQX1 iteration 15 Try 5 RMS(Cart)= 0.04284627 RMS(Int)= 1.07713453 XScale= 1.21113199 RedQX1 iteration 15 Try 6 RMS(Cart)= 0.07306699 RMS(Int)= 1.05687042 XScale= 1.21611671 RedQX1 iteration 16 Try 1 RMS(Cart)= 0.05584566 RMS(Int)= 1.12367314 XScale= 1.19765985 RedQX1 iteration 16 Try 2 RMS(Cart)= 0.02286496 RMS(Int)= 1.07499534 XScale= 1.21222322 RedQX1 iteration 16 Try 3 RMS(Cart)= 0.02438594 RMS(Int)= 1.12278813 XScale= 1.19799285 RedQX1 iteration 16 Try 4 RMS(Cart)= 0.04682132 RMS(Int)= 1.05848208 XScale= 1.22455630 RedQX1 iteration 16 Try 5 RMS(Cart)= 0.05180642 RMS(Int)= 1.12389433 XScale= 1.19724482 RedQX1 iteration 16 Try 6 RMS(Cart)= 0.09898022 RMS(Int)= 1.05217450 XScale= 1.22852267 RedQX1 iteration 17 Try 1 RMS(Cart)= 0.01405244 RMS(Int)= 1.05223388 XScale= 1.22927195 RedQX1 iteration 17 Try 2 RMS(Cart)= 0.01532275 RMS(Int)= 1.05308778 XScale= 1.22973513 RedQX1 iteration 17 Try 3 RMS(Cart)= 0.01774505 RMS(Int)= 1.05535773 XScale= 1.22948723 RedQX1 iteration 17 Try 4 RMS(Cart)= 0.02337632 RMS(Int)= 1.06214521 XScale= 1.22611476 RedQX1 iteration 17 Try 5 RMS(Cart)= 0.04777898 RMS(Int)= 1.07753330 XScale= 1.20250805 RedQX1 iteration 17 Try 6 RMS(Cart)= 0.30008798 RMS(Int)= 1.04123191 XScale= 1.21907521 RedQX1 iteration 18 Try 1 RMS(Cart)= 0.02979274 RMS(Int)= 1.03812188 XScale= 1.22464290 RedQX1 iteration 18 Try 2 RMS(Cart)= 0.03152106 RMS(Int)= 1.03630781 XScale= 1.23039806 RedQX1 iteration 18 Try 3 RMS(Cart)= 0.03488907 RMS(Int)= 1.03583467 XScale= 1.23669125 RedQX1 iteration 18 Try 4 RMS(Cart)= 0.04260264 RMS(Int)= 1.03648377 XScale= 1.24429916 RedQX1 iteration 18 Try 5 RMS(Cart)= 0.06643062 RMS(Int)= 1.03732218 XScale= 1.25513881 RedQX1 iteration 18 Try 6 RMS(Cart)= 0.04332704 RMS(Int)= 1.03485466 XScale= 1.26061916 RedQX1 iteration 19 Try 1 RMS(Cart)= 0.00647035 RMS(Int)= 1.03469781 XScale= 1.26054725 RedQX1 iteration 19 Try 2 RMS(Cart)= 0.00788731 RMS(Int)= 1.03474370 XScale= 1.26013856 RedQX1 iteration 19 Try 3 RMS(Cart)= 0.01031043 RMS(Int)= 1.03527770 XScale= 1.25902750 RedQX1 iteration 19 Try 4 RMS(Cart)= 0.01541658 RMS(Int)= 1.03739792 XScale= 1.25598323 RedQX1 iteration 19 Try 5 RMS(Cart)= 0.03279332 RMS(Int)= 1.05421092 XScale= 1.23882026 RedQX1 iteration 19 Try 6 RMS(Cart)= 0.05868770 RMS(Int)= 1.05419521 XScale= 1.22344191 RedQX1 iteration 20 Try 1 RMS(Cart)= 0.05119718 RMS(Int)= 1.06421641 XScale= 1.22470960 RedQX1 iteration 20 Try 2 RMS(Cart)= 0.01896880 RMS(Int)= 1.08792286 XScale= 1.20479268 RedQX1 iteration 20 Try 3 RMS(Cart)= 0.03527020 RMS(Int)= 1.05924323 XScale= 1.21536424 RedQX1 iteration 20 Try 4 RMS(Cart)= 0.13417975 RMS(Int)= 1.04029728 XScale= 1.23466810 RedQX1 iteration 20 Try 5 RMS(Cart)= 0.07499839 RMS(Int)= 1.05370992 XScale= 1.22678024 RedQX1 iteration 20 Try 6 RMS(Cart)= 0.05968335 RMS(Int)= 1.07297611 XScale= 1.20412241 Old curvilinear step not converged, using linear step: SCX= 1.09D+01 DXMaxT= 1.25-314 SCLim= 6.24-315 Fact= 5.71-316 RedCar/ORedCr failed for GTrans. Error termination via Lnk1e in C:\G09W\l101.exe at Tue Oct 13 15:55:28 2015. Job cpu time: 0 days 0 hours 0 minutes 1.0 seconds. File lengths (MBytes): RWF= 5 Int= 0 D2E= 0 Chk= 1 Scr= 1