5 CONCLUSIONS AND RECOMMENDATIONS
5.2 Recommendations for Future Work
Since the simulation time was significantly long, the optimization problem has been restricted to some certain amount of iterations and parameter values. Therefore, for further enhancement of the system, number of iterations and number of particles that are searching for the global extremes of the scheme can be increased and thus it might be possible to further refine the configuration of the PHEV components.
Furthermore, by implementing different control algorithms to the vehicle model a comparison can be made between the baseline models and the optimized component configurations, also, another comparison amongst each other might lead to a better approach in terms of finding better-tuned component sizes.
Last but not least, there are various kinds of gradient free optimization methodologies, because of the time restrictions and long simulation times, PSO assumed to work better than those other methodologies on PHEV powertrain component sizing systems by researching on the previous work done by different researchers. However, a better practice would be applying those optimization tools on the system and comparing them with PSO results. This might provide more confidence in the algorithm to choose and ensure the most proper optimization tool for such systems.
LIST OF REFERENCES
LIST OF REFERENCES
[1] J.P. Norbye and J. Dunne, "Hybrid Car Ready in 1969" Popular Science, p.
86-7, Jul. 1969.
[2] D. Assanis, G. Delagrammatikas, R. Fellini, Z. Filipi, J. Liedtke, N. Sales, and M. Sasene, “An optimization approach to hybrid electric propulsion system design,” Mech Struct Machines, 1999, 27(4), p. 393-421
[3] R. Fellini, N. Michelena, P. Papalambros, and M. Sasena, “Optimal design of automotive hybrid powertrain systems,” Proceedings of International Symposium ON Environmentally Conscious Design and Inverse Manufacturing EcoDesign’99, p. 400-405.
[4] V. Galdi, L. Ippolito, A. Piccolo, and A. Vaccaro, “A Genetic-Based Methodology for Hybrid Electric Vehicles Sizing,” Soft Computing 6, Springer-Verlag 2001.
[5] M. Montazeri-Gh, and A. Poursamad, “Optimization of component sizes in hybrid electric vehicle via genetic algorithms,” IMECE, ASME International Mechanical Engineering Congress and Exposion, Orlando, USA, 2005.
[6] Z. Zhengli, Z. Jianwu, and Y. Chengliang, “Optimization Approach for Hybrid Electric Vehicle Powertrain Design,” Chinese Journal of Mechanical Engineering, Vol. 18, No. 1, p. 30-36, 2005.
[7] X. Liu, Y. Wu, and J. Duan, “Optimal Sizing of a Series Hybrid Electric Vehicle Using a Hybrid Genetic Algorithm,” Proceedings of the IEEE, International Conference on Automation and Logistics, Jinan, China, August 18 – 21, 2007.
[8] A. Hasanzadeh, B. Asaei and A. Emadi. “Optimum design of series hybrid electric buses by genetic algorithm,” IEEE ISIE 2005, Dubrovnik, Croatia, p.1465-1470, June 2005.
[9] T. Markel, K. Wipke, “Optimization techniques for hybrid electric vehicle analysis using ADVISOR,” Proceedings of the ASME International Mechanical Engineering Congress and Exposition. New York, November 11-16, 2001.
[10] X. Wu, B. Cao, J. Wen, and Z. Wang, “Application of Particle Swarm Optimization for Component Sizes in Parallel Hybrid Electric Vehicles,”
IEEE Congress on Evolutionary Computation, p. 2874-2878, 2008.
[11] W. Gao and S. K. Porandla, “Design Optimization of a Parallel Hybrid Electric Powertrain,” IEEE Vehicle Power and Propulsion Conference, VPPC, p. 530-535, 2005.
[12] H. Banvait, S. Anwar and Y. Chen, “Plug-in Hybrid Electric Vehicle Energy Management System using Particle Swarm Optimization,” EVS24, Stavanger, Norway, May 13 - 16, 2009.
[13] H. Banvait, S. Anwar and Y. Chen, “A Rule-Based Energy Management Strategy for Plug-in Hybrid Electric Vehicle,” American Control Conference, St Louis, Missouri, June 10-12, 2009.
[14] J. Kennedy and R. Eberhart, “Particle Swarm Optimization,” Neural Networks, Proceedings, IEEE International Conference, Vol. 4, Pages1942-1948, 1995.
[15] Powertrain Systems Analysis Toolkit Documentation, Argonne National Laboratory, 2008.
[16] Q. Cao, S. Pagerit, R. B. Carlson, A. Rousseau, PHEV Hymotion Prius Model Validation and Control Improvements, EVS23, Anaheim, California, Dec 2-5, 2007.
[17] K. E. Parsopuulos and M. Vrahatis, “Particle Swarm Optimization Method for Constrained Optimization Problems,” Intelligent Technologies- Theory and application, IOS Press, p. 214-220, 2002.
[18] X. Wu, B. Cao,J. Wen, and Y. Bian, “Particle Swarm Optimization for Plug-in Hybrid Electric Vehicle Control Strategy Parameter,” IEEE Vehicle Power and Propulsion Conference (VPPC), Harbin, China, Sept 3-5, 2008.
[19] Y. Gao and M. Ehsani, “Design and Control Methodology of Plug-in Hybrid Electric Vehicles,” IEEE Vehicle Power and Propulsion Conference (VPPC), Harbin, China, September 3-5, 2008.
[20] X. Liu and Y. Wu, and J. Duan, “Optimal Sizing of a Series Hybrid Electric Vehicle Using a Hybrid Genetic Algorithm,” IEEE International Conference on Automation and Logistics, Jinan, China, October 2007.
[21] S. Moura and D. Calllaway,H. Fath, and J. Stein, ” Impact of Battery Sizing on Stochastic Optimal Power Management in Plug-in Hybrid Electric Vehicles,” IEEE International Conference on Vehicular Electronics and Safety, Columbus, OH, Sept 22-24, 2008.
[22] X. Hu and R. Eberhart. “Solving constrained nonlinear optimization problems with Particle Swarm Optimization,” Proceedings of the Sixth World Multi Conference on Systemics, Cybernatics and Informatics, Orlando, Florida 2002.
[23] E. T. Yildiz, Q. Farooqi, S. Anwar, Y. Chen, and Afshin Izadian, “Nonlinear Constrained Component Sizing of a Plug-in Hybrid Electric Vehicle“, EVS 25, Shenzhen, China, Nov. 5-9, 2010.
[24] Gregorio Toscano and Pulido C., “A constraint Handling Mechanism for Particle Swarm Optimization,” Evolutionary Computation, Vol. 2, p. 1396-1403, 2004.
APPENDICES
Appendix A Matlab Script
x(1,ii,1)=15+40.*rand;
x(2,ii,1)=30000+55000.*rand;
x(3,ii,1)=30000+50000.*rand;
v(1,ii,1)=15+60.*rand;
v(2,ii,1)=30000+55000.*rand;
v(3,ii,1)=20000+50000.*rand;
pbest_pos(:,ii)=x(:,ii,1);
%Change_Vars(x(1,ii,1),x(2,ii,1),x(3,ii,1));
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
load 5UDDS_PSAT_sim.mat
%% Changing Parameter Values Function
%% Files to Run ess_li_25_616_A123 ess_calculation
ess_cap_erg2pwr_ratio_s_lin
ess.scale.cap_max_des =x(1,ii,1) ;%%%%%%%%%%%%%%%%%%%%%xls read addition here
ess_cap_erg2pwr_ratio_s_lin
%%ess_old_mass=...
%%num of battery modules
%%% ENG variable change sequence eng_si_1497_57_US_04Prius
eng_old_mass=eng.init.block_mass+eng.init.tank_mass+eng.init.radiator_m ass;
eng_calculation eng_s_lin
eng.scale.pwr_max_des = x(2,ii,1);%%%%%%%%%%%%%%%%%%%xls read addition here
eng_s_lin
%%% Motor variable change sequence mc_pm_25_50_prius
mc_old_mass=mc.init.motor_mass+mc.init.controller_mass;
mc_pre_calculation mc_calculation mc_s
mc.scale.pwr_max_des =x(3,ii,1); %%%%%%%%%%%%%%%%%%%%xls read addition here
mc_s
% %%% Change the total mass of the vehicle
eng_new_mass=(eng.init.block_mass+eng.init.tank_mass+eng.init.radiator_
mass);
mc_new_mass=(mc.init.motor_mass+mc.init.controller_mass);
veh.init.mass=veh.init.mass+(eng_new_mass-eng_old_mass)+(mc_new_mass-mc_old_mass);
%% Re-Evaluate Controller files
%%% Propelling
p_stf_split_best_eng_MY04_US_prius
%%% Braking
b_stf_split_best_eng
%% Driveline Calculation driveline_s
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%
%sim('PSAT_mdl', [0:0.05:psat.global.gbl_stop_time]);
sim('PSAT_mdl', [0:0.01:1369*num_cyc]);
%%&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&%%
gal_cons=0.264172052*(max (eng_fuel_cum_simu)/0.749) ;%%
gallons consumed
mil_cyc=11990*0.000621371192*num_cyc;%%miles driven
if gal_cons==0
%%&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&%%
f(ii,1)=190/FC_cyc+x(1,ii,1)/55+x(2,ii,1)/85000+x(3,ii,1)/80000;
%%%%%%%%%%%%%%%%%%%%%%%%%%%% UPDATEEEEEE end
pbest(:,1)=f(:,1);%%%pbest(:, this dimension remain 1 always) [gbest,l]=min(f(:,1));gbest_pos(:,1)=x(:,l,1);
for kk= 2:max_iter, for ii=1:num_p,
v(1,ii,kk)=w.*(v(1,ii,kk-1)+2*rand(1)*(pbest_pos(1,ii)-x(1,ii,kk-1))+2*rand(1)*(gbest_pos(1,kk-1)-x(1,ii,kk-1)));
v(2,ii,kk)=w.*(v(2,ii,kk-1)+2*rand(1)*(pbest_pos(2,ii)-x(2,ii,kk-1))+2*rand(1)*(gbest_pos(2,kk-1)-x(2,ii,kk-1)));
v(3,ii,kk)=w.*(v(3,ii,kk-1)+2*rand(1)*(pbest_pos(3,ii)-x(3,ii,kk-1))+2*rand(1)*(gbest_pos(3,kk-1)-x(3,ii,kk-1)));
%Change_Vars (x(1,ii,kk),x(2,ii,kk),x(3,ii,kk));
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
load 5UDDS_PSAT_sim.mat
%% Changing Parameter Values Function
%% Files to Run ess_li_25_616_A123 ess_calculation
ess_cap_erg2pwr_ratio_s_lin
ess.scale.cap_max_des =x(1,ii,kk) ;%%%%%%%%%%%%%%%%%%%%%xls read addition here
ess_cap_erg2pwr_ratio_s_lin
%%ess_old_mass=...
%%num of battery modules
%%% ENG variable change sequence eng_si_1497_57_US_04Prius
eng_old_mass=eng.init.block_mass+eng.init.tank_mass+eng.init.radiator_m ass;
eng_calculation eng_s_lin
eng.scale.pwr_max_des = x(2,ii,kk);%%%%%%%%%%%%%%%%%%%xls read addition here
eng_s_lin
%%% Motor variable change sequence mc_pm_25_50_prius
mc_old_mass=mc.init.motor_mass+mc.init.controller_mass;
mc_pre_calculation mc_calculation mc_s
mc.scale.pwr_max_des =x(3,ii,kk); %%%%%%%%%%%%%%%%%%%%xls read addition here
mc_s
%%% Change the total mass of the vehicle
new_mass=(eng.init.block_mass+eng.init.tank_mass+eng.init.radiator_mass +mc.init.motor_mass+mc.init.controller_mass);
veh.init.mass=veh.init.mass+new_mass-(eng_old_mass+mc_old_mass);
%% Re-Evaluate Controller files
%%% Propelling
p_stf_split_best_eng_MY04_US_prius
%%% Braking
b_stf_split_best_eng
%% Driveline Calculation driveline_s
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%
%sim('PSAT_mdl', [0:0.05:psat.global.gbl_stop_time]);
sim('PSAT_mdl', [0:0.01:1369*num_cyc]);
ess_soc_simu %%to check if the variable is changing every iteration
%%&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&%%
gal_cons=0.264172052*(max (eng_fuel_cum_simu)/0.749) ;%%
gallons consumed
mil_cyc=11990*0.000621371192*num_cyc;%%miles driven
if gal_cons==0
%%&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&%%
f(ii,kk)=190/FC_cyc+x(1,ii,1)/55+x(2,ii,1)/85000+x(3,ii,1)/80000;
%%%%%%%%%%%%%%%%%%%%%%%%%%%% UPDATEEEEEE,NORMALIZE
if pbest(ii,1)> f(ii,kk) pbest(ii,1)=f(ii,kk);
pbest_pos(:,ii)=x(:,ii,kk);
end end
[gbest,l]=min(pbest(:,1));%%%%%%%%%%%%GLOBAL BEST IS THE BEST AMONG THE PBEST VALUES, SINCE THE PBEST VALUES ARE STORING THE BEST EACH PARTICLE FOUND SO FAR
gbest_pos(:,kk)=pbest_pos(:,l);
end
Appendix B Objective Function Values
x(:,:,1) = 1.0e+004 *
Columns 1 through 6
0.0053 0.0018 0.0029 0.0052 0.0016 0.0027
5.5336 6.1931 8.2896 6.3140 5.5967 6.5776
4.4382 4.4351 7.7678 6.5107 6.2254 7.4570
Columns 7 through 10
0.0051 0.0041 0.0040 0.0021 4.4109 6.0055 8.4235 7.4601 7.8340 4.2566 4.0358 5.3825
x(:,:,2) = 1.0e+005 *
Columns 1 through 6
0.0007 0.0005 0.0006 0.0011 0.0005 0.0009
1.1736 1.3180 1.5515 1.1432 1.1445 1.0732
0.7204 1.0727 1.2846 0.9768 0.9454 1.1452
Columns 7 through 10
0.0010 0.0006 0.0010 0.0004 0.8320 1.0289 1.6298 1.5277 1.3965 0.8062 0.8397 1.0558
x(:,:,3) = 1.0e+005 *
Columns 1 through 6
0.0006 0.0007 0.0006 0.0012 0.0006 0.0011
1.3612 1.5276 1.7468 1.2961 1.3306 1.1949
0.8032 1.2614 1.2530 0.9708 0.9476 1.1048
Columns 7 through 10
0.0012 0.0005 0.0011 0.0005 0.9830 1.1646 1.8173 1.7441 1.4190 0.9270 0.9896 1.1647
x(:,:,4) = 1.0e+005 *
Columns 1 through 6
0.0003 0.0003 0.0004 0.0005 0.0004 0.0007
0.9884 1.0946 1.5917 1.0184 1.1626 1.0121
0.6563 0.7525 0.9140 0.4836 0.7923 0.9522
Columns 7 through 10
0.0009 0.0003 0.0009 0.0004 0.8408 0.8488 1.3817 1.3055 0.8536 0.6585 0.7185 0.9167
x(:,:,5) =
1.0e+005 *
Columns 1 through 6
0.0002 0.0000 0.0001 0.0000 0.0001 0.0005
0.3978 0.7032 0.6357 0.3394 0.7706 0.7988
0.3517 0.3681 0.2497 0.0638 0.4894 0.4557
Columns 7 through 10
0.0002 0.0000 0.0001 0.0002 0.3781 0.3274 1.0679 0.3938 0.0907 0.2711 0.2564 0.3119
x(:,:,6) = 1.0e+004 *
Columns 1 through 6
0.0015 0.0001 0.0001 0.0001 0.0001 0.0006
0.0568 2.4570 0.1000 0.5457 2.1168 4.2054
1.5540 0.1000 0.1000 0.1000 2.9378 1.9948
Columns 7 through 10
0.0001 0.0001 0.0001 0.0001 0.1000 0.1000 6.8828 0.1000 0.1000 0.1000 0.1000 0.1000
x(:,:,7) = 1.0e+004 *
Columns 1 through 6
0.0023 0.0001 0.0001 0.0024 0.0001 0.0001
0.1000 0.9256 0.1000 1.1316 0.1000 2.4824
1.6778 0.1000 0.6395 5.2664 2.2732 2.6590
Columns 7 through 10
0.0001 0.0025 0.0001 0.0001 0.1000 0.8336 3.5347 0.6699 1.6111 0.1000 0.9652 0.0197
x(:,:,8) = 1.0e+005 *
Columns 1 through 6
0.0003 0.0001 0.0001 0.0005 0.0001 0.0000
0.4241 0.4296 0.6867 0.6333 0.2733 0.2937
0.3454 0.3325 0.2549 1.1599 0.2995 0.5738
Columns 7 through 10
0.0000 0.0004 0.0003 0.0001 0.2460 0.3623 0.2597 0.5128 0.8559 0.0417 0.1926 0.3834
x(:,:,9) = 1.0e+005 *
Columns 1 through 6
0.0004 0.0002 0.0003 0.0006 0.0002
0.0001
0.9824 1.0338 1.0395 1.1377 0.6920 0.5359
0.5290 0.7478 0.8223 1.3310 0.4120 0.7601
Columns 7 through 10
0.0001 0.0005 0.0006 0.0002 0.7488 0.9101 0.4685 1.2130 1.2411 0.2726 0.3159 0.8373
x(:,:,10)
=
1.0e+005 *
Columns 1 through 6
0.0005 0.0003 0.0004 0.0005 0.0003 0.0003
1.2283 1.3732 1.1573 1.2839 0.9686 0.7633
0.6516 0.9399 1.1067 0.8764 0.6056 0.8832
Columns 7 through 10
0.0001 0.0004 0.0006 0.0002 1.1913 1.2496 1.0057 1.4880 1.2499 0.6542 0.4171 1.0606
x(:,:,11)
=
1.0e+005 *
Columns 1 through 6
0.0005 0.0003 0.0004 0.0003 0.0003 0.0004
1.0562 1.1999 1.0709 1.0394 1.0233 0.9488
0.6655 0.7551 1.1635 0.6641 0.7268 0.7960
Columns 7 through 10
0.0003 0.0003 0.0005 0.0003 1.1197 1.2153 1.2649 1.2276 1.0119 0.8826 0.5308 0.9645
x(:,:,12)
=
1.0e+005 *
Columns 1 through 6
0.0003 0.0002 0.0003 0.0001 0.0002 0.0004
0.6396 0.6992 0.8159 0.5514 0.8079 0.9118
0.6119 0.3437 0.6362 0.4079 0.7299 0.6355
Columns 7 through 10
0.0004 0.0003 0.0004 0.0003 0.3739 0.9635 1.1283 0.8182 0.4954 0.8340 0.5677 0.5098
x(:,:,13)
=
1.0e+004 *
Columns 1 through 6
0.0028 0.0006 0.0020 0.0013 0.0015 0.0026
0.7037 2.4423 7.2063 3.2813 3.2017
8.3998
3.9732 0.1000 2.1176 2.4012 5.9819 4.7682
Columns 7 through 10
0.0044 0.0026 0.0038 0.0019 0.1000 6.6168 5.3296 3.0218 0.1000 6.4864 4.7369 2.1458
x(:,:,14)
=
1.0e+004 *
Columns 1 through 6
0.0021 0.0001 0.0007 0.0019 0.0009 0.0001
0.1000 0.9022 6.6185 2.9514 1.1440 5.5540
1.8720 0.1000 0.6317 2.8767 4.9581 4.0967
Columns 7 through 10
0.0040 0.0029 0.0028 0.0013 0.1000 2.4410 2.0609 1.0194 0.1000 2.1502 3.1128 1.4087
x(:,:,15)
=
1.0e+004 *
Columns 1 through 6
0.0022 0.0010 0.0005 0.0028 0.0007 0.0001
2.6734 2.7938 6.0653 3.3966 0.8579 4.1131
1.4978 1.5604 2.7878 5.3828 4.2651 3.7874
Columns 7 through 10
0.0036 0.0032 0.0018 0.0011 4.6060 0.6307 1.8291 1.0546 2.6114 0.1000 2.3614 2.5748
x(:,:,16)
=
1.0e+004 *
Columns 1 through 6
0.0038 0.0027 0.0017 0.0035 0.0012 0.0006
5.5708 9.2767 5.8977 6.5851 3.2401 4.4262
3.1471 3.6535 8.2301 7.5794 4.5627 4.8890
Columns 7 through 10
0.0027 0.0036 0.0017 0.0012 7.8363 2.5459 5.1950 5.6604 6.6198 0.6193 2.8996 4.5841
x(:,:,17)
=
1.0e+005 *
Columns 1 through 6
0.0005 0.0004 0.0003 0.0005 0.0002 0.0002
0.9045 1.3239 0.6444 1.0401 0.7290 0.5534
0.4180 0.5542 1.3588 0.8376 0.5647
0.6289
Columns 7 through 10
0.0002 0.0003 0.0002 0.0002 0.9380 0.6539 0.9314 0.8235 1.0512 0.3313 0.4152 0.6269
x(:,:,18)
=
1.0e+005 *
Columns 1 through 6
0.0005 0.0004 0.0004 0.0005 0.0002 0.0003
1.0329 1.1785 0.7437 1.1326 0.9298 0.7656
0.5538 0.6478 1.3274 0.7021 0.6309 0.7798
Columns 7 through 10
0.0003 0.0003 0.0002 0.0003 0.8544 0.8367 1.1305 0.9550 1.1819 0.6531 0.5166 0.6913
x(:,:,19)
=
1.0e+005 *
Columns 1 through 6
0.0004 0.0003 0.0004 0.0004 0.0002 0.0005
0.7874 0.8261 0.7781 0.9621 0.9150 0.8797
0.6169 0.5987 0.7941 0.3825 0.6595 0.8555
Columns 7 through 10
0.0004 0.0002 0.0003 0.0003 0.6675 0.8747 0.9838 0.9278 1.0312 0.7938 0.5488 0.6304
x(:,:,20)
=
1.0e+004 *
Columns 1 through 6
0.0025 0.0019 0.0026 0.0025 0.0021 0.0043
4.7361 3.5996 7.3021 5.9519 6.4451 8.3905
5.5105 4.8872 3.2775 1.7398 5.7131 7.2613
Columns 7 through 10
0.0045 0.0022 0.0037 0.0028 4.6452 7.3144 7.1950 7.7790 4.5810 6.6383 5.1960 4.0500
x(:,:,21)
=
1.0e+004 *
Columns 1 through 6
0.0026 0.0010 0.0018 0.0018 0.0016 0.0030
2.5110 0.7719 6.6023 3.4460 4.1298 6.6190
3.9369 3.5566 1.2057 1.9836 4.3967 5.0163
Columns 7 through 10
0.0032 0.0021 0.0040 0.0019 3.6483 6.3622 4.8809 5.7762 0.1000 5.0038 3.7335 2.9322
x(:,:,22)
=
1.0e+004 *
Columns 1 through 6
0.0038 0.0006 0.0015 0.0020 0.0012 0.0006
1.9473 2.4096 5.9252 2.8079 3.3102 5.1312
2.4113 2.8476 1.3192 3.8939 3.9785 3.5000
Columns 7 through 10
0.0016 0.0021 0.0036 0.0008 3.3524 5.1983 3.9364 4.5548 0.1000 2.2306 2.8729 2.9919
x(:,:,23)
=
1.0e+004 *
Columns 1 through 6
0.0052 0.0008 0.0016 0.0034 0.0011 0.0001
4.4048 5.8298 6.6058 3.6845 3.7462 4.6028
2.0985 3.0054 3.1139 7.7027 4.4173 3.8533
Columns 7 through 10
0.0011 0.0027 0.0030 0.0004 4.5906 4.6610 5.0912 4.8002 5.2192 1.1892 2.9158 4.9480
x(:,:,24)
=
1.0e+005 *
Columns 1 through 6
0.0006 0.0001 0.0002 0.0004 0.0001 0.0000
0.7609 0.9384 0.7011 0.5574 0.4768 0.4904
0.3910 0.3463 0.5140 1.0415 0.4722 0.5114
Columns 7 through 10
0.0002 0.0003 0.0002 0.0001 0.6572 0.5214 0.7752 0.6014 1.2340 0.1139 0.4286 0.5960
x(:,:,25)
=
1.0e+005 *
Columns 1 through 6
0.0005 0.0002 0.0003 0.0004 0.0002 0.0001
0.8836 1.0760 0.7990 0.7770 0.5993 0.6249
0.5512 0.4732 0.7897 1.0876 0.5649 0.7043
Columns 7 through 10
0.0005 0.0004 0.0002 0.0002 0.7865 0.6169 0.9404 0.7913 1.4917 0.2936 0.5249 0.6115
x(:,:,26)
=
1.0e+005 *
Columns 1 through 6
0.0004 0.0003 0.0003 0.0004 0.0002 0.0003
0.7906 1.0041 0.8429 0.8568 0.7150 0.7526
0.6408 0.5808 0.9622 0.6845 0.6316 0.7565
Columns 7 through 10
0.0007 0.0004 0.0002 0.0003 0.6854 0.6745 0.9161 0.8891 1.0375 0.6321 0.5510 0.5381
x(:,:,27)
=
1.0e+004 *
Columns 1 through 6
0.0019 0.0026 0.0029 0.0037 0.0022 0.0041
5.8303 8.6117 8.1272 8.2314 7.3999 8.0688
5.9048 5.9168 8.4716 2.5952 6.4889 7.7379
Columns 7 through 10
0.0073 0.0035 0.0033 0.0038
5.7577 6.8929 7.5235 8.3699 4.3998 7.8654 5.0191 4.0551
x(:,:,28)
=
1.0e+004 *
Columns 1 through 6
0.0013 0.0023 0.0026 0.0036 0.0017 0.0037
3.7344 6.0664 6.9169 5.7993 6.2926 7.2287
4.1373 5.2432 6.9601 0.3856 6.2907 6.7329
Columns 7 through 10
0.0070 0.0025 0.0044 0.0022 4.2966 6.5288 6.0551 7.2746 1.1081 6.4992 3.7342 3.4758
x(:,:,29)
=
1.0e+004 *
Columns 1 through 6
0.0013 0.0017 0.0023 0.0037 0.0012 0.0027
3.0764 4.3263 6.0803 3.5986 4.9030 5.9897
1.9046 4.3962 5.5071 2.4690 5.9130 5.8370
Columns 7 through 10
0.0030 0.0021 0.0042 0.0000 3.4889 6.0882 5.6682 5.9125
1.2838 3.3244 2.7654 3.5205
x(:,:,30)
=
1.0e+004 *
Columns 1 through 6
0.0032 0.0011 0.0021 0.0033 0.0010 0.0009
3.5644 3.9379 5.5450 3.1582 4.3037 5.1214
1.4304 3.6035 4.6426 6.0252 5.7555 5.1504
Columns 7 through 10
0.0000 0.0019 0.0040 0.0001 3.3327 5.6521 6.4933 5.1792 3.6137 0.9583 2.8141 4.1345