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TAP CHi KHOA HOC VA C O N G NGHE Tap 47, s6 6, 2009 Tr 117-129 ANALYZING AND OPTIMIZING OF A PFLUGER COLUMN TRAN DUC TRUNG, BUI HAI LE ABSTRACT The optimal shape of a Pfiuger column is determined by using Pontryagin's maximum principle (PMP). The governing equation of the problem is reduced to a boundary-value problem for a single second order nonlinear differential equation. The results of the analysis problem are obtained by Spectral method. Necessary conditions for the maximum value of the first eigenvalue corresponding to given column volume are established to determine the optimal distribution of cross-sectional area along the column axis. Keywords: optimal shape; Pontryagin's maximum principle. 1. INTRODUCTION The problem of determining the shape of a column that is the strongest against buckling is an important engineering one. The PMP has been widely used in finding out the optimal shape of the above-mentioned problem. Tran and Nguyen [12] used the PMP to study the optimal shape of a column loaded by an axially concentrated force. Szymczak [11] considered the problem of extreme critical conservative loads of torsional buckling for axially compressed thin walled columns with variable, within given limits, bisymmetric I cross-section basing on the PMP. Atanackovic and Simic [4] determined the optimal shape of a Pfiuger column using the PMP, numerical integration and Ritz method. Glavardanov and Atanackovic [9] formulated and solved the problem of determining the shape of an elastic rod stable against buckling and having minimal volume, the rod was loaded by a concentrated force and a couple at its ends, the PMP was used to determine the optimal shape of the rod. Atanackovic and Novakovic [3] used the PMP to determine the optimal shape of an elastic compressed column on elastic, Winkler type foundation. The optimality conditions for the case of bimodal optimization were derived. The optimal cross-sectional area function was determined from the solution of a nonlinear boundary value problem. Jelicic and Atanackovic [10] determined the shape of the lightest rotating column that is stable against buckling, positioned in a constant gravity field, oriented along the column axis. The optimality conditions were derived by using the PMP. Optimal cross-sectional area was obtained from the solution of a non-linear boundary value problem. Atanackovic [2] used the PMP to determine the shape of the strongest column positioned in a constant gravity field, simply supported at the lower end and clamped at upper end (with the possibility of axial sliding). It was shown that the cross-sectional area function is determined from the solution of a nonlinear boundary value problem. Braun [5] presented the optimal shape of a compressed rotating rod which maintains stability against buckling. In the rod modeling, extensibility along the rod axis and shear stress were taken into account. Using the PMP, the optimization problem 17 is formulated with a fourth order boundary value problem. The optimally shaped compressed rotating (fixed-free) rod has a finite cross-sectional area on the free end. In this paper we determine the optimal shape of a Pfiuger column - a simply supported column loaded by uniformly distributed follower type of load (see Atanackovic and Simic [4]). Such load has the direction of the tangent to the column axis in any configuration and does not have a potential, i.e., it is a non-conservative load. The results of the analysis problem are obtained by Spectral method. PMP allows estimating the maximum value of the Hamiltonian function that satisfies the Hamiltonian adjoint equations instead of solving the minimum objective functions directly. An analogy between adjoint variables and original variables holds for some cases. This is an advantageous condition to determine the maximum value of the Hamiltonian function. Although PMP have been investigated, the objective function is still implicit, the sign of the analogy coefficient k is indirectly determined and the upper and lower values of the control variable are unbounded. The present work suggests a method of supposition to determine k directly and exactly. The Maier functional, which depends on state variables in fixed locations, is used as the objective function from a multicriteria optimization viewpoint. The bounded values are set up for the control variable. The present paper is organized as follows: following the introduction section is presented formulation of the problem, optimization problem is considered in section 3, results and discussion are given in section 4, and final remarks are summarized in section 5. 2. FORMULATION OF THE PROBLEM The formulation of the problem is established basing on Atanackovic and Simic [4] and Atanackovic [1]: Consider a column shown in Fig. 1. The column is simply supported at both ends with end C movable. The axis of the column is initially straight and the column is loaded by uniformly distributed follower type of load of constant intensity q^. We shall assume that the column axis has length L and that it is inextensible. Let x-B-y be a Cartesian coordinate system with the origin at the point B and with the x axis oriented along the column axis in the undeformed state. The equilibrium equations could now be derived dH dV = -q-, dS ^' — = -q,; dS ' dM ,, n TT • r, = -Fcos6'-hi/sin6* (2.1) dS where H and V are components of the resultant force (a force representing the influence of the part {S, L] on the part [0, S) of the column) along the x anAy axis, respectively, Af is the bending moment and 6 is the angle between the tangent to the column axis and x axis. Also in (2.1) q'v and qy are components of the distributed forces along the x and y axis respectively. Since the distributed force is tangent to the column axis we have q,=-qf^cosd; qy=-q^sm9 (2.2) To the system (2.1) we adjoin the following geometrical 118 — = cos^; ^ dS dS = sin^ (2.3) and constitutive relation (2.4) y-i-civ M+dM Figure 1. Coordinate system and load configuration In (2.3) and (2.4) we use x and y to denote coordinates of an arbitrary point of the column axis and EIXo denote the bending rigidity. The boundary conditions corresponding to the column shown in Fig. 1 are x(0) = 0; :i'(0) = 0; M(0) = 0; y{L) = Q', M{L) = 0; H{L) = 0. (2.5) The system (2.1)—(2.5) possesses a trivial solution in which column axis remains straight, i.e., Ff{S) = -qo{L-S)', 1^(5) = 5; A / ( 5 ) = 0; .x\^ = S', / ( 5 ) = 0; ^{S) = 0. (2.6) In order to formulate the minimum volume problem for the column we take the crosssectional a r e a ^ ( ^ and the second moment of inertia I{S) of the cross-section in the form A{S)=Aoa{S)', I{S) = Ioa-{^ (2.7) where Ao and /o are constants (having dimensions of area and second moment of inertia, respectively) and a{S) is cross-sectional area function. For the case of a column with circular cross section we have the connection between AQ and /Q given by IQ = {\I4'K)A^ . Let AH,..., Ad be the perturbations of//,..., 6*defined by H = lf+AH', V=l^+AV', M = Af+AM',x=x°+Ax',y=y°+Ay', d=^+A0. (2.8) qJl (2.9) Then, by introducing the following dimensionless quantities , AHL' EL'0 AVI} Eh ^-"o AMI EL , Ax L n Ay L S / = —; L X EL and by substituting (2.7) in (2.1) - (2.5) we arrive to the following nonlinear system of equations describing nontrivial configuration of the column 119 h= -A{\-cosd); V = -As'md; m = -vcos0-\-[-/l{\-t) + h]s\n0; ^ = l-eos^; • (2.10) ;7 = sin6'; m 2 • where (•) = d{»)/ dt. The boundary conditions corresponding to (2.10) are ^0) = 0; ;7(0) = 0; w(0) = 0; rj{]) = 0; w(l) = 0; h{\) = 0. (2.11) Note that the system (2.10)-(2.11) has the solution /?(/) = 0,..., 0{t) = 0 for all values of A. Next we linearize (2.10) to obtain h^O; v^-Ad; ih^-v + -X{\-t)9; 1 = 0; (2.12) m 9= ^ . w By using boundary conditions (2.11) in (2.12) we conclude that h{t) = <^t) = 0 and the rest of Eqs (2.12) could be reduced to rh-^ — {l-t)m a =0 (2.13) . , subject to: w(0) = w(I) = 0. The system (2.13)-(2.14) constitutes a spectral problem. ' (2.14) 3. OPTIMIZATION PROBLEM To determine the optimal shape of the column, we will use the PMP (Geering [8]). Let us write optimization problem as: find out a{t), a„,„ < a{t) < a,Mx, t e [0, 1], satisfies the objective function G = -{\-k^j)\+k^,J = 'cmn. (2.15) where X\ is the first dimensionless eigenvalue, k^ is non-negative weight, k;^ e [0, 1], the dimensionless volume of the column J is defined as 1 J=\a{t)dt 0 , The state differential equations are 120 . (2.16) X, =x, ; x,=-^{\~t)x, (2.17J subject to • x,(0) = X2(l) = 0. (2.18) TL Proposition.' with the above-mentioned suppositions, Eqs. (2.15) - (2.18), the Hamiltonian function H is maximized, and the analogy coefficient k betM'een adjoint variables and original variables is positive, where: -4~^{\-i)x: H=k (2.19) • k^jO = max (in a). a Proof. The first eigenvalue A] is here considered as a state variable. It means that the role of/I] is equivalent to those of xi and Xi in the state differential equations (2.17). The volume of the column J is also a state variable. So, the state equations (2.17) can be rewritten in the form 1 A (1-Ox, jio; ;r 'If.: . . . 2 (2.20) a' I = a of the Maier's objective functional: The objective function can be rewritten inJterm G = -(l-A:,,)^(l) + ^,,J(l) = min. (2.21) From the Eqs. (2.20) the Hamiltonian function //can be established in the form as follows ^ = /'.vl^'2+P.2 -^0-OA-, \ + p,,A,+pja, ^=0. (2.22) The adjoint equations can be expressed in the following form: a// _ A, p..I (2.23a) OX, Cf ! i fi dH _ p.l (2.23b) dx., dH 0/1, 1 „ The conjugate variables p.rP.xi^Pn^P.i (2.23c) a dH_ PJ , 0 (2.23d) ' dJ ^''^ determined from the expression: (2.24) ,=i , = 1 Thus p,, (l)^x, (1) + p^, {l)Sx, (1) + p , , (1)^.^ (1) + PJ (1)^^(1) -p,,(O)Jx,(O)-p,,(O)Jx,(O)-p„(O)^-^(0)-p,(O)<5J(0)-(l-^,,)&^(l) + /r,,(5J(l) = O (2.25) 121 or p,f\)Sxfl) + p,f\)5xf\) + [p,fl)-{\- -p^, {0)5 x, (0) - p^, {0)5 X, {0)-p,, k„)] ^-^ (1) + [;;, (1) + k,, ]5J{\) {0)5A, (0) - p, {0)SJ{0) = 0. (2.26) Hence P.2 (1) = P..2 (0) = 0; p,, (1) = 1 - k,_,; p „ (0) = 0; p, (1) = -k,_,; p, (0) = 0 (2.27) Assigning (2.28) Px\ ~ ~^2H ' Px2 ~ ^\H we obtain ^ ^\H (2.29) 7 (1-0^1//• ^7H ' ^71 -^JH subject to x^f^ (1) = x,^ (0) = 0 . (2.30) It is seen that Eqs. (2.17) are similar in form as ones of (2.29) and the boundary conditions (2.18) are also similar in form as the conditions (2.30). As a result, we reached the following conclusion: the same analogy between the adjoint variables and the original variables holds, or KX,f, (2.31) — X,, /OC-,, The sign of ^ can be determined by integrating the Eq. (2.23c) with appropriate conditions in Eq. (2.27): \p^^dt^p,f\)~pJQ) = \~k„=\f^^^dt>Q. 0 "^ 0 (2.32 '^ Thus, the sign of the analogy coefficient k is larger than zero for the case of maximizing X\. It was demonstrated by considering the first eigenvalue A\ as a state variable. The Hamiltonian function (2.22) will be maximized if H= x\-\(\-t)xl •k^jU = max (in a). (2.19) Thus, basing on the PMP in optimal control for above-mentioned system's first eigenvalue, the obtained optimal necessary conditions consist of: the state equations (2.17), the boundary conditions (2.18), the control variable a(t) e fa,^,„, a^^J and the maximum conidition of the Hamiltonian function (2.19). a{t) (initial) i: Analysis module ^ Converged \, * • 1 False a{t) <—'Optimization module (new) Figure 2. The general algorithm used in the present work 122 Results a{f), Xx,J From the multicriteria optimization viewpoint, the Parcto front between the criterion (/>.,. J) is build basing on the Definition 6 in Coello Coello et al. [6] (page 10): A solution x e Q is said lo be Parelo-optimal M'ith respect to Q f and only if there is no x" e Q for which v = F(\') = (fi(\'').....f(\")) dominates u = F(\)=(f(\)....J,,(x)). The phrase Pareto-optimcd is meant with respect lo the entire decision variable space unless otherwise specified. In words, this definition says that x* is Pareto-optimal if there exists no feasible vector x which would decrease some criterion without causing a simultaneous increase in at least one other criterion (assuming minimization). 4. RESULTS AND DISCUSSION 4.1. Validation of the model In order to verify results obtained in the present work, the model in Atanackovic and Simic [4] is studied for both validation analysis and optimization problems. 4.1.1. Analysis problem The first eigenvalue of the studied column with constant circular cross-section was shown in Table 1. Table I. The first eigenvalue of the studied column .'. \ The first eigenvalue X\ Methods Present a{t)=\ 18.957240 t7(/) = 0.81051 12.453513 Atanackovic and Simic [4] 18.956266 12.452807 4.1.2. Optimization problem We take J = 0.81051, 0 < a{l) < co. The aim of this section is to determine the column's optimal shape (optimal distribution of circular cross-sectional area) and maximum value of X\ according to above input data. The resuhs are shown in Table 2 and Fig. 3. Table 2. The maximum value of Ai Methods Present 18.950876 Atanackovic and Simic [4] 18.956266 Via sections 4.1.1 and 4.1.2, it is evident that the results of the authors, those of Atanackovic and Simic [4] are in good agreement, (see Atanackovic and Simic [4] to compare the column's optimal shape). 123 C3 "cd c o 15 0.4 0.2 0.3 0.4 0.5 0.6 0.7 O.J 0.9 The normalized length / Figure 3. The column's optimal shape 4.2. Results and discussion for the optimization problem of the authors The content of the problem consists in finding out the changing rule of the circular crosssection a{t) 6 [amin, <3niax], t £ [0, 1] which satisfiBS the state differential equations (2.17); maximizing the first eigenvalue Ai; the total volume J of the column is given. We take Umm = 0-9; fl™x= 1-1-Thus, J 6 [0.9, 1.1]. 4.2.1. Optimization problem with above-mentioned input data The results shown in Table 3 and Fig. 4 are the maximum values of A] {Ai^asi), the column's optimal s'lape configurations corresponding to five cases of J. Table 3. The maximum values of A] (Ai^axi) corresponding to five cases of J i n the section 4.2.1 " Notation J -^Imaxl Case la 1.100 (/„/,,) 22.937693 (i,,,;,,) Case 2a 1.050 22.817520 Case 3a 1.000 21.570576 Case 4a 0.950 18.705883 Case 5a 0.900 (J„.i) 15.354985 (/l|/„„,,) The Pareto front or trade-off curve which includes the set of points that bounds the bottom of the feasible region is shown in Fig. 5. 124 Where, /Ipari ("/") ^nd Jpari (%) are the normalized variation of the maximum value of the first eigenvalue and the total volume, respectively. ; 100(/l, -^^^^,), /l-Parl = , 100(J-J, ,) -'Pari = ^^ •^ i(p\ 2i^ -^ lm>\ -r ^ •Case la ' Case 2a "Case 3a ' Case 4a "Case 5a 1.05 0.95 0.9- I I I I I M I I I h 0.1 I I I 1 I r'l I I I 0.2 IIIIII 0.3 M I I IM1 I I 0.4 0.5 0.6 The normalized length / IIK 0.7 1 0.8 I 0.9 Fig. 4. The column's optimal shape configurations corresponding to five cases of J in the section 4.2. 4 60 i 10 20 30 40 50 60 70 80 90 Variation of Jp^^\ , % Figure 5. The Paretofi-ontof the optimization problem in the section 4.2.1. 125 4.2.2. Optimization problem with above-mentioned input data and an additional constraint The additional constraint in this section is that a{t) = 1, ? e [0.1, 0.2]. It means that the distribution of the cross-sectional area along the column axis is discontinuous. The results described in the Table 4, Fig. 5 & 6 are the maximum values of A\ {AU^XT), the column's optimal shape configurations corresponding to five cases of 7and the Pareto front. Table 4. The maximum values of A\ (/liniax2) corresponding to five cases of J in the section 4.2.2 Notation J A 1 ma\2 Case lb 1.089 (7,„,2) 22.431435 (i,,,;,,) Case 2b 1.050 22.386033 Case 3b 1 21.487410 Case 4b 0.950 18.427398 Case 5b 0.911 (J/„„,2) 15.661187 (A„„„.2) 03 15 C O 1.05- o •Case Case •Case Case 'Case 111111111 -o N •ig. 6, (J. 95 o c H 0.9' 11 l l 1111 lb 2b 3b 4b 5b I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I II I I I I I I II I ll I I I M I II I I I I I I II I I I II I II I I I II 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 The normalized lensth / Figure 6. The column's optimal shape configurations corresponding to five cases of J in the section 4.2. Where, A^^a (%) and Jpar2 (%) are the normalized variation of the maximum value of the first eigenvalue and the total volume, respectively. Ap lOO(^„,2-^,„ax2) '\up2 126 , 100(J-J,„,0 •7pai2- ^lou'2 f up2 "^ lOM-l