The Optimization Analysis of Navigation Performance and Structural Properties of the High-Speed Monohull Ship in River

Transcription

1 Available online at Phyic Procedia 33 (2012 ) International Conference on Medical Phyic and Biomedical Engineering The Optimization Analyi of Navigation Performance and Structural Propertie of the High-Speed Monohull Ship in River HE Yijia 1,YANG Songlin 1, YANG Zhao-long 2 CHENG Haibian 1,LI- Mi 1 1 Jiangu Univerity of Science and Technology,Zhenjiang, Jiangu, , China 2 Jiangu Univerity,Zhenjiang, Jiangu, China @163.com, yl560516@vip.163.com,chenghaibian1314@126.com,yzl5588@gmail.com Abtract The author ue the weighting ummation of rapidity and maneuverability a the ub-objective function of hip performance; By uing the weighting ummation of tatic and dynamic propertie a the ub-objective function, The weighting ummation of thee 2 ub-objective function i jut the general objective function. In thi paper, a parallel multi-proceing genetic chao algorithm(pm-c-fga) ha been put forward baed on fuzzy method, genetic algorithm and chao algorithm. Thi algorithm applied effectively to optimize the navigation performance and tructural configuration of high-peed mono-hull hip in river Publihed by Elevier by Elevier B.V. Selection Ltd. Selection and/or peer and/or review peer-review under reponibility under reponibility of ICMPBE International of name Committee. organizer Open acce under CC BY-NC-ND licene. Keyword-integrate optimization; PM-C-FGA algorithm; high-peed monohull hip;navigational performance; tructural propertie Introduction The mot engineering deign quetion are the multi-objective iue. For example, when working on the mechanical ytem reliability deign we hope the ytem ha high reliability, low cot, light weight and o on. Thi quetion of demanding everal deign indexe reached optimum at the ame time i called multi-objective optimization quetion. Meanwhile in the deign proce the fuzzine i inevitable. The hip deign i the typical complex multi-objective deign quetion with fuzzine. When we deign a hip we hould conider the navigation performance, tructural configuration and arrangement characteritic. And their deign parameter and contrained condition have a certain boundary uncertainty Publihed by Elevier B.V. Selection and/or peer review under reponibility of ICMPBE International Committee. Open acce under CC BY-NC-ND licene. doi: /j.phpro

2 834 HE Yijia et al. / Phyic Procedia 33 ( 2012 ) In order to olve thi kind of problem, in 1993, Profeor Xu Changwen1 developed the multi objective Fuzzy optimization invetigation in tructural engineering. In 1994, Zeng Guangwu2 made an advancement in optimum deign method of hip tructure. In 2002, Liu Chang3 made the Fuzzy optimization methodology of hip ea-keeping performance. In 2002, Zhang Huoming 4 made to Fuzzy genetic Algorithm of Ship Navigation Performance Optimization. In 2007, the author 5 made up the fuzzy-chao algorithm which i compoed of the fuzzy genetic algorithm of bound earch and the chao algorithm Thi algorithm i applied to optimize calculate and analyze large-cale hip performance or tructural characteritic In thi paper, a parallel multi-proceing genetic chao algorithm (PM-C-FGA) ha been put forward baed on fuzzy method, genetic algorithm and chao algorithm. Thi algorithm applied effectively to optimize the navigation performance and tructural configuration of high-peed mono-hull hip in river. Pm-C-Fga PM-C-FGA i: According to the enitivity of deign variable, we divided variable into the mot enitive deign variable, econd-enitive deign variable, third-enitive deign variable and nonenitive deign variable, partition the earch area thoe enitive deign variable cope by principle that high degree of enitivity have many partition, then interect and combine variable earch cope zone. Thoe combination and remaining deign variable earch cope compoe everal optimization earch cope. Optimization calculation have two tep. Firtly, earch zone and hort-algebra calculation of the parallel chaotic optimization at the ame time. Second, electing 3 or 5 group of bet optimal reult, each group a a new optimization earch zone, then do parallel fuzzy-ga calculation to obtain the optimal olution which i expected to efficiently for multi-objective, multi-dicipline and multi-variable optimal olution of complex engineering problem. A. FGA Eential Procedure FGA i the algorithm baed on delimitation earch, which i to run genetic algorithm on pecial level when fuzzy optimization. It i explained with delimitation earch method on fuzzy optimization and genetic algorithm. The delimitation earch method that can preent a ditinct olution on fuzzy nonlinear programming i a common fuzzy optimization about engineering deign. The book6 i read about it tep. The genetic algorithm key tep adopted in thi article are a follow: Coding: The chromoome coding i the floating data coding. Selection: In thi paper the comparatively common roulette method i employed. Croover: The croover probability p c i elected a Mutation: The mutation probability p m i et a Fitne and Evaluation halt computing rule4. B. Chao Algorithm Chao optimization i implemented by chao variable. The author chooe a widely-ued Logitic mapping to produce the chao variable: z k+1 = z k (1-z k ) Where the time of iterative mapping k=0,1,2,. It eay to prove that when =4, above equation i fully in chao tate, which mean by iterative mapping, the equation can randomly produce all value within (0, 1) except 0.25, 0.5 and Becaue chao algorithm i enitive to initial value, n different chao variable can be obtained by aigning n

3 HE Yijia et al. / Phyic Procedia 33 ( 2012 ) different initial value within (0, 1) to the equation except 0.25, 0.5 and 0.75.In thi paper, the author adopt twice-mapping chao algorithm 5. Mathematical Model There re 2 part of ynthetical optimization of hip performance and tructure characteritic: navigation performance and tructural mechanic propertie. Stability, buoyancy and ome other characteritic a well a limit of deign variable form the contraint condition. The mathematic model i decribed in detail a follow: C. Objective Function Suppoe P(X) i the general objective function, P N (X) i the ub-objective function of navigation performance, and F(X) i the ub-objective function of tructural mechanic propertie. Then, P(X) =P N (X) Ap * F(X) Bp P N (X) =C p Ap 1*M v Ap 2 Where Cpand Mv are repectively normalized form within 0,1 of rapidity criterion C and maneuverability criterion M. F(X) i gravity per meter of longitudinal member of midhip ection.then, H hull efficiency; 2/3 V 3 o R H C p =P E / Where diplacement; P E effective power; o crew efficiency in the open; R relative rotation efficiency. M=V pl pt arl *V art Where V arl traight line tability coefficient; V art turning quality coefficient; m F ( xp ) (X) ( V V V )/ L l ti tb i 1 V Where, M i the number of tranvere frame of typical tank; i the denity of material; l i the V volume of longitudinal member of typical tank; ti i the volume of ith tranvere frame of typical tank; Vtb i the volume of ingle tranvere bulkhead of typical tank; L i the length of typical tank. D. Contrant condition Equation contraint 1) Balance between buoyancy and diplaced weight: LBTC B = 2) Balance between effective thrut T E and reitance R; 3) Balance between torque received by crew from main engine M d and torque from hydrodynamic reitance M p. 4) Structure contraint: the tre of tank member are hown in the following Table 1. Inequality contraint 1) Range of 49 deign variable value; 2) Cavitation requirement for crew propeller according to Kelly formula;

4 836 HE Yijia et al. / Phyic Procedia 33 ( 2012 ) ) Initial tability height G M >h; 4) Maximal rolling angle a b ; 5) Relative turning diameter D <c. Table 1. Structure Contraint Member type Inner and bottom plate, ide plate Bottom plate longitudinal, ide longitudinal Tranvere bulkhead, centrline bulkhead, knee Deck and platform tructure Platform longitudinal, deck longitudinal, ide longitudinal P: Pillar permiible tre i material buckling trength, MPa E. Deign variable The ynthetical optimization of mechanic propertie for hip involve many factor. After analyzing and comparing their importance, 49 parameter (including 35 parameter of midhip ection) are elected a the main deign variable: hip length L, hip breadth B, draft T, longitudinal primatic coefficient Cp, mid-hip ection coefficient CM, water plane coefficient CWP, longitudinal poition of buoyancy center xcb, diameter of crew propeller DP, dik area ratio AE/AO, pitch ratio P/DP, rotation peed of propeller N, target velocity Vt, half angle of entrance ie, wetted urface area ratio of flap At/Am, thickne of upper deck 1, thickne of upper deck 2, type of upper deck longitudinal, thickne of upper deck grider, thickne of ide plating 1, thickne of ide plating 2, thickne of ide plating 3, type of ide longitudinal, thickne of ide tringer, thickne of pillar, thickne of tween deck, type of tween deck longitudinal, thickne of tween deck grider, thickne of bilge trake 1, thickne of bilge trake 2, thickne of bilge trake 3, type of bilge trake longitudinal, thickne of bilge trake grider, thickne of longitudinal bulkhead, type of longitudinal bulkhead longitudinal, thickne of tranvere bulkhead, type of tranvere bulkhead longitudinal, thickne of inner bottom plating, type of inner bottom longitudinal, thickne of bottom plating, thickne of flat keel, type of bottom longitudinal, thickne of centre girder, thickne of ide girder, longitudinal pace of upper deck, ide longitudinal pace, longitudinal pace of tween deck, longitudinal pace of bulkhead, longitudinal pace of bilge plating, longitudinal pace between inner plating and bottom plating. Their vector i a follow: X={x 1, x 2, x 3, x 4, x 5, x 6, x 7, x 8, x 9, x 10, x 11, x 12, x 13, x 14, x 15, x 16, x 17, x 18, x 19, x 20, x 21, x 22, x 23, x 24, x 25, x 26, x 27, x 28, x 29, x 30, x 31, x 32, x 33, x 34, x 35, x 36, x 37, x 38, x 39, x 40, x 41, x 42, x 43, x 44, x 45, x 46, x 47, x 48, x 49 }. Example Of Optimization Computation

5 HE Yijia et al. / Phyic Procedia 33 ( 2012 ) F. Optimization computaation The mathematic model how that the ynthetical optimization of mechanic propertie for high-peed monohul hip in river involve at leat 49 deign variable, 9 equation contraint and 5 inequality contrain. Evidently, it a very complicated engineering optimization. the author programme the olving oftware. Here take a high-peed monohul hip in river for example. It diplacement i 4250t and it ha double propeller. The range of it deign variable value are lited in table. 3: The author aign value a: Ap=1.0, B P =1.25; A P1 =1.05, A P2 =1.0;pl=1.35;pt=1.55 The author run chao algorithm,ga of 7000 generation, parallel GA of 500 generation and PM-C- FGA. The reult are a TABLE2&3 how: Table 2. Calculation Reult Of Different Method Item Chao algorithm GA (7000 generation ) Parallel GA (500 generation ) PM-C- FGA (300 generation) General objective function value Diplacement T E (kn) Reitance M p (kn m) M d (kn m) o P E (kw) Main engine power (kw) Froude number (Fr) Initial tability height(m) Wetted urface Relative turning diameter D Friction drag modulu Cf Re e e e e +009 Table3. Main Deign Variable Limit And Reult Of Optimization Item Lower limit Upper limit reult X 1 (m)

6 838 HE Yijia et al. / Phyic Procedia 33 ( 2012 ) X 2 (m) X 3 (m) X X X X X 8 (m) X X X X 12 (kn ) X X From the reult, we can ee that inequality contraint are all atified to a degree of 100%. Thee indicate that thi olving method i reliable. G. Analyi From the table 32 we can gain the atifaction of condition of equality contraint on hip performance. The reult are hown in the following table 3. From the table 2, we can ee that the atifaction of condition of equality contraint on hip performance i higher than 99.77%. Thee indicate that the penalty trategy i efficient. 3 point of concluion are drawn after comparing and analyzing thoe different olving method from table 3. a.the value of chao algorithm and 7000-generationed GA algorithm general objective function are repectively and The former i lower than the latter by 7.17%, which mean parallel algorithm i more efficient. b.the value of 7000-generationed GA and 500-generationed parallel GA general objective function are repectively and The former i lower than the latter by 9.60%, which mean obviou premature convergence of GA. c. PM-C-FGA general objective function i It higher than thoe of parallel GA algorithm and GA by 2.35% and 13.23%. Thee 2 data tell u that PM-C-FGA baed on delicate variable egment i the bet among thee method in olving complicated engineering optimization of multiobjective, multi-contraint and multi-variable. Concluion In thi paper, PM-C- FGA ha been put forward to applying to ynthetic optimization of hip performance and tructure characteritic for high peed monohull hip in river. Computation reult how

7 HE Yijia et al. / Phyic Procedia 33 ( 2012 ) that thi method i of high efficiency. It lay on a olid foundation for overall evaluation of high peed monohull hip in river deign and integrated deciion of hip parameter. Uing thi oftware can provide the condition to integrate evaluation of the hip deign project and the integrate deciion-making of hip parameter. Reference 1Xu Changwen. The development of multi objective fuzzy optimization invetigaton in tructural engineering. journal of hanghai intitute of building material,1993 (3): Zeng Guangwu.Advance in Optimum Deign Method of Ship Structure.computational tructural mechanic and application,1994(1): Liu Chang. Study on the Fuzzy Optimization Methodology of Ship S Seakeeping Performance. Journal of eat China hipbuilding intitute(natural cience edition),2002(6): Zhang Huoming. Fuzzy genetic Algorithm of Ship Navigation Performance Optimization. Shipbuilding 0f China,2002(3): Yang Songlin etc. Fuzzy-chao algorithm and it tudy on application of integrate optimization of hip perform ance or tructural characteritic, Journal of Ship Mechanic2007(2): Yang Song-lin. Method of engineering fuzzy and it application. Beijing: National Defence Indutry Pre, 1996 (in Chinee) 7Yang Songlin etc. Optimizing-computation of controlling parameter of intelligent propulion ytem of a hydrofoil liding craft propelled by adjutable-pitch crew, NCM2009, Seoul, Korea Augut ,,2009 ** HI-TECH RESEARCH AND DEVELOPMENT PROGRAM OF CHINA (2006AA11Z220)