MATLAB implementation

This section describes the implementation of the PCE methodology formulated in Section 3.5.2.1 in the MATLAB code “RelAnalysisHMV-PCE.m”. As exposed previously, the SORA consists of solving iteratively a DO followed by a RA. The PCE will be implemented in the RA phase, particularly before the execution of the HMV algorithm. The idea is to build a surrogate model that approximates the limit-state functions Gi depending on the random variable values and use it when the HMV requires the evaluation of G(uk) and ∇uG(uk) to obtain the MPP as indicated in Section2.4.2.2.

When the DO phase of the SORA is finished, the LHS of the random variables is performed through the MATLAB command lhsnorm(µ, σ, Ns), where µ and σ are the mean and standard deviation values of the Gaussian random variables x and Ns is the number of samples. The input points obtained from the LHS are stored in a (Ns× nr) matrix denoted as Xsampl, where the number of rows Ns is the number of samples and the number of columns nr the number of random variables. When the random variables are not Gaussian, the one-dimensional polynomials selected to construct the

PCE cannot be Hermite polynomials ψ. Instead they must be the ones presented in Table 3.1 depending on the random distribution considered. After the sampling, the matrix of input random variables Xsamplis transformed to the matrix of normalized and uncorrelated variables Usampl through the general Rosenblatt tranformation u = T (x) for two reasons:

1. The HMV algorithm requires working with standarized variables for obtaining the limit-state function G(u).

2. It is easier to built the one-dimensional polynomials ψ(u) through Equation 3.13

if the random variabless follow a standarized probabilistic distribution.

The next step is to obtain the structural responses of all the sample points. This task is easily parallelizable and allows to exploit the potential of the HPCC in a similar way that was exposed in Section 3.5.1 when the algorithm that drives the DO is a population based method. In this sense, each row of Xsampl contains the informa- tion of a single sample, so “Abaqus-ParAnalysis.m” creates Ns folders through a for loop and the GNU system command mkdir. Afterwards it prints each row of the matrix in a .py file that contains the FE model and which is moved to the correspond- ing folder, having at the end a number Ns of .py files placed each one in a different folder. Then the FE analyses are executed simultaneously through the system com- mand “qsub lanzador-abaqus-jobID”, where jobID denotes the unique identifier to the file that manages the task running lanzador-abaqus. The steps followed within the for loop going from 1 to Ns are presented below:

1. Create Folder i.

2. Print in the .py file the values of random variables stored in Xsampl. 3. Rename .py file as “Job-i.py” and copy to Folder i.

4. Run “Job-i.py” through “system (qsub lanzador-abaqus-jobID)”

The responses of each sample are retrieved to the file “Samples.txt”. They are read by MATLAB and stored in a single-column vector Ysampl with Ns rows.

Having Xsampl, Usampl and Ysampl allows to build a PCE of the form Equation 3.12 and obtain the unknown coefficients bi. In the polynomial expansion implemented in MATLAB, Ysampl represent the real responses R and Usampl are utilized to construct the Hermite polynomials Ψ. All these steps are carried out through the MATLAB code “Regress-CoeffPCE.m”, which requires as an input the order p of the PCE and the number of samples Ns, and whose steps are detailed below.

First, a 3-D matrix of dimensions (Ns×(p+1)×nr) designated as Herm is constructed (Figure3.9), where Ns is the number of sample points, p is the maximum order of the one-dimensional Hermite polynomial ψi(ui) and nr is the number of random variables.

This means that Herm contains in each horizontal layer (plane xz) the values of the one-dimensional polynomials ψi from order “0” to “p” (ψ0, ψ1, ..., ψp) evaluated in the standarized random variables u (u1, u2, ..., unr), and the y dimension corresponds to

the number of samples. A representation of the 3-D matrix Herm and one of single horizontal layers are presented in Figure3.9.

1-D Herm. polynomials ψ0, ..., ψp Random vars. u1, ..., unr No. of Samples S1 ,..., SN s Herm Layer 1 Layer 2 ... Layer Ns x y z

(a) 3-D matrix Herm

x 1-D Hermite polynomials ψ0, ..., ψp z Random v ariables u1 , ..., un r ψ0(un) ψ1(un) ψ2(un) ψp(un) ψp(u3) ψ2(u3) ψ1(u3) ψ0(u3) ψ0(u2) ψ1(u2) ψ2(u2) ψp(u2) ψ0(u1) ψ1(u1) ψ2(u1) ψp(u1) ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...

(b) Horizontal layer of Herm for one sample

Figure 3.9: Representation of the 3-D matrix Herm

Afterwards, the i order one-dimensional Hermite polynomials evaluated in each random variable ψi(uj) are combined as presented in Section 3.5.2.1 in order to build the k multidimensional Hermite polynomials Ψk required and store them in 2-D matrix denoted as Xmatrix. The number of Ψ is the same as unknown coefficients in the regression Nc, which is obtained through Equation3.16 and thereby the dimensions of

Xmatrix are (Ns × Nc). For example, in a second-order PCE (p = 2) for two random

variables (nr = 2) whose number of terms is 6, each row of the Xmatrix is created as exposed in Figure 3.10.

For higher order PCE or more random variables, the number of terms of the PCE increases a lot. Hence the combinations of the one-dimensional ψi(uj) in order to create the multivariate Ψk(u) become more complex. In this cases it is necessary to apply combinatorial operators such as combinations or permutations that are established in MATLAB using the command nchoosek(u, k), which returns a matrix containing all the possible combinations of the elements of vector u taken k at a time. The matrix has k columns and nr!

(nr−k)!k! rows, being nr the length of u.

The last step is to obtain the regression coefficients bkthrough linear regression models. This can be done as exposed in Equation3.21or using the MATLAB internal function LinearM odel.f it. The latter provides, besides the coefficients, further interesting

Ψ0(u) Ψ1(u) Ψ2(u) Ψ3(u) Ψ4(u) Ψ5(u) ψ₀(u₁)ψ₀(u₂) = 1 ψ1(u1)ψ0(u2) = u1 ψ0(u1)ψ1(u2) = u2 ψ₂(u₁)ψ₀(u₂) = u2₁− 1 ψ0(u1)ψ2(u2) = u22− 1 ψ₁(u₁)ψ₁(u₂) = u₁u₂

Figure 3.10: Creation of a generic row of Xmatrix.

information about the regression model, which is stored in a MATLAB structure. The unknown coefficients are stored in a vector denoted as RCoeff. Finally, the fitted model bR is computed as Equation 3.22, obtaining Yregres as:

Yregres = Xmatrix× RCoeff (3.27)

Afterwards, the main MATLAB code assess the accuracy of Yregres against the real responses Ysample through the ANOVA table and the statistical estimators R2 and RM SE explained in Section3.5.2.2. All of them have been implemented in the main code, but can be obtained as well from LinearM odel.f it. If the R2 _{estimator is not} accurate enough (less than 0.9) or the ANOVA table evidences that all the coefficients of the regression are significant (F0 > Fα,1,df e), the regression model is re-built increas- ing the order of the PCE expansion as exposed in the flowchart of Figure 3.11. The sequence of steps required to build the polynomial expansion is detailed below:

1. Determine the order of the PCE.

2. Select experimental designs using a LHS sheme.

3. Perform the transformation of the random variables Xsample → Usample.

4. Obtain system responses Ysample at each design point through FE simulations. 5. Build a surrogate model Yregres using the PCE.

6. Assess the accuracy of the metamodel through ANOVA, R2 and RM SE. 7. If the accuracy of the metamodel is enough, stop. Otherwise, increase the order

Determine order of the PCE expansion Sampling of random vari-

ables Xsamplusing LHS

Xsampl → Usampl

Obtain sample

responses Ysampl

ABAQUS Parallelisation

Construct 3-D matrix Herm with 1-D Hermite polynomials

Construct 2-D matrix Xmatrix

containing Ncmultidimen-

sional Hermite polynomials

Obtain unknown coefficients b = RCoeff Metamodel accuracy: ANOVA, R2_{and RM SE} “Regress-Co effPCE.m ” Build Y r eg r es Adequate

fitting? Increase order of the PCE

Yregres

yes

no

Figure 3.11: Steps required to build the metamodel.

Once the polynomial expansion is built up and the accuracy of the metamodel is proven, the RA is carried out through the HMV algorithm, which is implemented in the MATLAB code “HMV-PCE.m”. The steps followed by “HMV-PCE.m” are the same than in “ReliabilityAnalysisHMV.m”. The only difference lies in the calculation of the derivatives ∇uG(uk) in the k iteration. In this case, instead of performing a finite difference process, they are obtained analytically by differentiating the polynomial expansion bR respect to the uncorrelated random variables u (d bR/du). This is achieved by obtaining the set of derivatives of the ith order one-dimensional Hermite polynomials presented in Equation 3.15.

 dψ_i(u) du



=0, 1, 2u, 3u2− 3, 4u3_{− 12u, 5u}4_{− 30u}2 _{+ 15, ...}

The derivatives with respect to each uiare evaluated at each sample and combined in a similar way than in Xmatrix, leading to a 3-D matrix denoted as XDmatrix. This matrix has dimensions of (Ns× Nc× nr). It can be said that the frontal plane xy of the matrix is similar to Xmatrix, but instead of having the values of the multidimensional Hermite polynomials Ψ it has the values of the one-dimensional derivatives with respect to u1 combined. The z dimension denotes the partial derivatives with respect to the rest of the random variables ui, from 2 to nr. Figure 3.12 presents a single row of the xy plane of the XDmatrix that corresponds to the row presented in Figure 3.10 when is derived with respect to u1. This procedure is analogous to the rest of the z dimension.

dΨ0(u)

du1 dΨ1(u)du1 dΨ2(u)du1 dΨ3(u)du1 dΨ4(u)du1 dΨ5(u)du1 d(ψ0(u1)ψ0(u2)) du1 = d1 du1 = 0 d(ψ1(u1)ψ0(u2)) du1 = du1 du1 = 1 d(ψ0(u1)ψ1(u2)) du1 = du2 du1 = 0 d(ψ2(u1)ψ0(u2)) du1 = d(u2_{1 −1))} du1 = 2u1 d(ψ0(u1)ψ2(u2)) du1 = d(u2_{2 −1))} du1 = 0 d(ψ1(u1)ψ1(u2)) du1 = d(u1u2)) du1 = u2

Figure 3.12: Creation of the row of XDmatrix corresponding to the row of Fig-

ure3.10.

Therefore, the derivatives of the structural response with respect to the random vari- ables d bR/du, which are denoted as YDregres, are obtained as follows:

YDregres = XDmatrix× RCoeff (3.29)

The gradients of the limit-state function at each iteration k of the RA loop are ∇uG(uk) =

n

d bR/du1, d bR/du1, ..., d bR/dunr

o .

Moreover, the statistical moments of the structural response R are obtained from the coefficients of the PCE through the implementation of Equation 3.23.

The flowchart of the whole RA process including sampling (LHS), metamodel building through a PCE and the steps of the HMV algorithm benefiting from the PCE-LHS based metamodel is presented in Figure 3.13.

Build metamodel using PCE-LHS

Perform RA through HMV

Obtain response at each design point Select experimental designs using LHS Obtain coefficients of the regression Construct approx- imation surface ABAQUS Parallelisation Search for convexity of G Calculate normalize steepest direction Obtain G(uk) and ∇G(uk) Initial MPP (k = 0) xk=µx; uk=0 Calculate uk+1 Convergence? uMPP_{= u} k+1 k = k + 1 PCE-LHS αk = − ∇G(uk) k∇G(uk)k ξ = (αk+1− αk) · (αk− αi,k−1) uk+1= βT· αk uk+1= βT· αk+αk−1+αk−2 kαk+αk−1αk−2k yes no ξ > 0 or k ≤ 3 ξ ≤ 0 and k > 3

Figure 3.13: Flowchart of the Reliability Analysis process including metamodel building.

To summarize, the flowchart containing all the steps followed by the methodology using Abaqus and population based algorithms in the DO phase and the polynomial chaos expansion in the RA phase is presented in Figure 3.14.

Initial RBDO Design (K=0) DV: d0 MPP: x0 _{= µ} x Deterministic Optimization Flowchart of Figure3.8 d∗ Reliability Analysis HMV Flowchart of Figure3.13 u∗ = MPP Convergence in Objective Function? K = K + 1 Update FEM with:

DV: d0 MPP: xK+1_{= µ} x+ σx· u∗ Optimum design d∗, u∗ yes no

Figure 3.14: Flowchart of the proposed methodology when the DO phase is per- formed with Abaqus and GA and the RA phase with the PCE.

3.6

Conclusions

This chapter has proposed an implementation of the SORA method, which is a de- coupled RBDO algorithm that separates the Deterministic Optimization and the Re- liability Analysis phases, in a MATLAB computational code that allows to perform complex and industrial-like aerospace RBDO problems. This methodology is versa- tile since it allows to solve a broad variety of optimization problems in an efficient way. Thanks to the decoupled nature of the SORA, the MATLAB code can be eas- ily combined with external deterministic optimization solvers that are widely used in aerospace companies (i.e. Altair Optistruct). In this way uncertainty can be incor- porated to several optimization types such as size, shape and topology. Moreover, the methodology proposed is also suitable for other optimization approaches that are not yet fully implemented in commercial optimization softwares (discrete, multiobjec- tive...). In such cases the DO phase is driven through optimization algorithms already

implemented in the MATLAB Optimization Toolbox, being the objective function and constraints obtained through FE analyses that are performed in FE commercial codes (i.e. Abaqus). In this sense, if the optimization problem can be solved effectively through external optimization software the methodology implemented allows to take profit of this and otherwise the methodology benefits from optimization algorithms included in the MATLAB Optimization Toolbox while the structural responses are obtained from a FE software.

Furthermore, the Reliability Analysis (RA) phase is solved through the Hybrid Mean Value (HMV) algorithm, which is appropiate for concave and convex limit-state func- tions and is as well implemented in the MATLAB code. This algorithm is a combina- tion of the AMV and CMV and has been selected because it is the most appropiate for a general limit-state function, overcoming the instabilities and inefficiency of the AMV and CMV for concave and convex functions, respectively. In the HMV, the structural responses of the structure are necessary to build both the limit-state function and its gradients. These responses are obtained through FE analyses that are performed in Altair Optistruct or Abaqus, depending on the approach selected in the DO phase. When the RA phase requires FE analyses that are computationally very expensive in order to obtain the structural responses, the methodology benefits from metamodelling techniques based on the Polynomial Chaos Expansion (PCE) that are implemented in MATLAB for saving computational time.

The following chapters will focus on the application of the aforementioned methodology to industry-like examples of aerospace structures. More concretely, Chapter 4 will present three practical applications of aerospace topology optimization problems (a wing, a rear fuselage and a pylon) involving a large number of design variables and degrees of freedom, while Chapter6will show an application of discrete optimization in a stiffened composite panel considering the post-buckling behaviour, aiming to obtain the maximum load before collapse by changing the stacking sequence layup.

Application of reliability-based

topology optimization to aerospace

structures

4.1

Introduction

Aerospace industry is always seeking for new and novel aircraft architectures as well as redesigning classical components in order to address the next generation of aircrafts, which tend to be ligther, cleaner and safer, making the application of Deterministic Topology Optimization (DTO) techniques a useful tool for reaching these targets. This discipline is being used since the last decade by most of the major aircraft manufactur- ers, such as Airbus (Grihon et al [76], Krog et al [119] or Krog et al [120]), Boeing (Rao et al [172] or Wang et al [207]), Bombardier (Buchanan [29]) or Embraer (Santos and Rocha De [178]), as well as many researchers in the aerospace field (Maute and Allen [142], Stanford and Ifju [194], Dunning et al [59], Stanford and Dunning [193] or James et al [106]) due to its capability to originate unconventional and disruptive structural configurations that are usually lighter than the classical ones and that could not be derived from an initial engineering judgement. In this framework, Zhu et al [228] draws a detailed survey about recent advances in topology optimization techniques applied to the design of aircraft and aerospace structures.

Topology optimization was used in the design of Airbus A380, the largest commercial aircraft in the world, leading to an overall weight savings of 700 kg per aircraft. This was mainly thanks to the redesign of the inboard inner and outer fixed leading edge ribs and the fuselage door intercostals (Grihon et al [76], Krog et al [121]). Moreover, in Krog et al [120] new wing box rib architectures were obtained through topology op- timization using global and local approaches combining several different formulations. These include the classical minimum global compliance formulation and a mini-max global compliance formulation, both with a volume fraction constraint, as well as an

alternative minimum weight with constrained compliance formulation. More recently, the Airbus A350 XWB benefited from topology optimization for the conceptual design of main componentes such as the pylon (Remouchamps et al [173]). Bombardier has also used topology optimization in the novel design of ribs within an aircraft wingbox applying a minimum global compliance with volume fraction constraint formulation (Buchanan [29]). Boeing also applied this discipline to the preliminary design of the wing leading-edge structure of the 787 Dreamliner, reaching to a 25–45% lighter design compared with the wing of the Boeing 777 (Wang et al [207]).

Furthermore, aircraft design needs to take into account several uncertain data to address the best possible design, while accomplishing the restrictive safety targets imposed by international regulations. Uncertain data in loads, material properties, manufacture processes, etc. need to be included as random variables in the different aircraft design phases, particularly during the preliminary stage where DTO is applied. Reliability-Based Topology Optimization (RBTO) combines statistical and probabilis- tic design methods with topology optimization, and is being used by several author of different research fields (Kim et al [114], Kim et al [115], Kang and Luo [110] or Dunning et al [58]). This discipline requires high computationally effort and may be unstable in terms of convergence depending on the problem. RBTO techniques are based on Reliability-Based Design Optimization (RBDO) methods, however they have not yet been applied to large three-dimensional FE models and hence it has not been proven that works properly for real like aerostructures.

In this chapter the APPROACH 1 presented in Section 3.2 has been applied to some examples of DTO and RBTO of practical aerospace components and structures. Sec- tion 4.2 introduces a brief explanation of the most widely used topology optimization methods, while Section 4.3 presents two different strategies to perform RBTO de- pending on the nature of the design region considered. Next, Section 4.4 exhibits industry-like application examples of a wing, a rear fuselage and a pylon. To end, Section4.5 drawn the main conclusions arisen from the chapter.