Research Article  Open Access
Topology Optimization Using Parabolic Aggregation Function with IndependentContinuousMapping Method
Abstract
This paper concentrates on finding the optimal distribution for continuum structure such that the structural weight with stress constraints is minimized where the physical design domain is discretized by finite elements. A novel IndependentContinuousMapping (ICM) method is proposed to convert equivalently the binary design variables which is used to indicate material or void in the various elements to independent continuous design variables. Moreover, three smooth mappings about weight, stiffness, and stress of the structural elements are introduced to formulate the objective function based on the socalled concepts of polish function and weighting filter function. A new general continuous approach for topology optimization is given which can eliminate the stress singularity phenomena more efficiently than the traditional relaxation method, and an alternative strain energy method for the stress constraints is proposed to overcome the difficulty in stress sensitivity analyses. Mathematically, by means of a generalized aggregation KSlike function defined as the parabolic aggregation function, a topology optimization model is formulated with the weight objective and single parabolic global strain energy constraints. The numerical examples demonstrate that the proposed methods effectively remove the stress concentrations and generate blackandwhite designs for practically sized problems.
1. Introduction
Topology optimization is one of the most challenging research topics but also a research focus in the current structural optimization design field, which has been rapidly developed with a lot of fruitful research work in the past two decades, and the development of topology optimization can be found in the monographs [1–3] and references therein. However, the representative methods to structural optimization design for variable topologies are homogenization method, SIMP method, ESO method and Levelset method, and so forth.
Homogenization method is a very important continuous nonlinear optimization method for topology optimization in structural design [4–13]. This method assigns the orientation and geometry size of the microstructure as the design variables, in which the microstructure is deleted according to the changes in the unicellular size, and leads to anisotropic materials with the middlesized microstructures. By transforming the difficult structural topology optimization model into a relatively easier sizing optimization model based on the microstructural parameters of composite materials, the approach of homogenization is capable of finding the optimum topologies of the structures. Since this formulation has two kinds of design variables to each element, the sensitivity analysis and the solution process become complicated. However, homogenization method often produces designs with infinitesimal pores in the materials that make the structure unnecessary strong and nonmanufacturing.
After the original homogenizationbased method, the solid isotropic material with penalization (SIMP) approach has been developed and well established as its conceptual and practical simplicity [6, 14–16] with applications into many areas such as fluids, multiphysics and multidisciplinary applications, and much more [1, 17–20]. As a variant of the homogenization method, the SIMP method introduces artificially the material with variable pseudodensity between 01 by allowing the material to take intermediate densities. However, a general nonlinear interpolation optimization model for the material properties is formulated, and the density of each finite element is chosen as the design variable in which its relationship with the Young’s modulus is expressed by an empirical formula. It was proved that the physical microstructures for SIMP scheme would exist for the satisfaction of some simple conditions on the penalty exponent [16]. Some additional schemes proposed by Sigmund [21] and Petersson and Sigmund [22] should be considered so as to eliminate numerical instabilities such as the checkerboards and meshdependency.
Evolutionary structural optimization method referred to as ESO has been presented by Xie and Steven [23, 24] which is successfully applied to optimum material distribution problems for continuum structures including stress considerations, frequency optimization, and stiffness constraints so as to gradually remove material and achieve an optimal design. However, the ESO method is based on the evolutionary strategy in the optimization process by gradually removing ineffective or inefficient material from the ground structure to get the topology plots through each iteration. In order to improve and perfect the traditional ESO method, its later development bidirectional ESO (BESO) is proposed by Querin et al. [25], Yang et al. [26], and Rozvany [19], which allows elements to be added in the locations next to those elements with highest sensitivity values as well as to be eliminated in the region with lowest sensitivity values. For the hardkill and softkill methods in the ESO method, Huang and Xie [27] proposed a new softkill BESO method based on the material interpolation scheme with penalization which is the general case of the original hardkill BESO method [28], when the exponent is not tended to infinity.
Another class of approaches is essentially based on the stress distribution on the current boundaries, which is called as the level set method proposed by Sethian and Wiegmann [29] so as to capture design boundary movements on a fixed Eulerian mesh. In the standard level set method, the structural boundary is described as the zero level set of a higherdimensional scalar function by an implicit level model (HamiltonJacobi PDE) and the rigorous shape derivative [13, 30–32]. Luo et al. [33, 34] proposed the alternative full parameterization level set method for structural shape and topology optimization problems using radial basis functions with compact support, in which the structural boundary is still represented implicitly as the zero level set of the level set function by taking into account the merit of the implicitfree boundary representation so as to bridge the standard level set method with many optimization algorithms. However, the level set method has a good flexibility to describe the topology of complex structures and boundary change.
The structural topology optimization may come down to solve the optimal distribution problems in which subsystems of the overall structure that should be filled with material and should be void, where the skeleton structures including truss structures and frame structures are optimized by the elimination or restoration of some elements and the continuum structures are optimized by making some subdomain into empty or restoration. In practice, the design domain is usually discretized into finite elements in order to obtain a finite dimensional problem with zeroone binary variables, indicating presence or absence of material in the various finite elements, but this leads to integer nonlinear programs which are very hard to solve a global optimum even for relatively small problems [35–37]. Since the largescale integer programming for solving the current problems has no efficient algorithms, people usually use the lowlevel design variables such as size or materials instead of the traditional zeroone binary topological design variables. Homogenization method attaches the topological variables on the size of material microstructure, and SIMP method attaches the topological variables on the Young’s modulus.
However, each abovementioned scheme has its pros and cons; and thus, there is still room left for improvement. Hence, the ideal scheme for topology optimization still has to be invented.
The paper is organized as follows: in Section 2, the independentcontinuousmapping (ICM) method for topology optimization problems is described. The ICM method abstracts the topology variables from lowlevel design variables such as the crosssection, thickness, and density so as to be independent of the traditional physical quantities and uses continuous variables in instead of 0 and 1 binary discrete variables to avoid the difficulties of discrete optimization. Moreover, three smooth mappings about weight, stiffness, and stress of the structural elements are introduced to formulate the objective function based on the socalled concepts of polish function and weighting filter function to complete the transformation of topological variables from discrete to continuous and to the discrete by the approximation of continuous variables with discrete topology variables. In Section 3, an alternative strain energy method for the topology optimization problem with local stress constraints is presented based on the von Mises criteria in the theory of elastic failure and the theory of strain energy under tridirectional stress state to overcome the difficulty in stress sensitivity analyses. In Section 4, a new generalized aggregation KSlike function defined as the parabolic aggregation function is derived, which is different from the two common global functions such as norm and KS function, and an optimization model is formulated with the weight objective and a single global constraints. In Section 5, the serial Lagrange multiplier method (SLMM) is applied to deal with the structural reanalysis of the nonlinear topology optimization problems, and implemental issues are discussed. In Section 6, three widely studied numerical examples are presented to demonstrate the effectiveness of the proposed topology optimization method, where conclusions are drawn in the end of the paper.
2. IndependentContinuousMapping (ICM) Method
In this section, we give a brief introduction to the independentcontinuousmapping structural optimization method which is based on the idea presented by Sui [38], Sui and Yang [39], Sui [40] and Sui et al. [41, 42].
2.1. Domain Setting and the Independent Topological Design Variables
Let us consider a fixed design domain in in topology optimization of load carrying continuum structures. The infinite dimensional topology optimization problem is to find the subdomain filled with material (or the subdomain occupied by void) such that a structure of optimum performance is obtained while constraints are satisfied, where is an indicator function that defines the distribution of material [1, 6]
Suppose that the fixed design domain is discretized into a ground structure with finite elements, we can define the binary design variables as when the th element is filled with material and when the th element is void such that the problem is then to decide the binary design variable vector .
Now, we consider the general binary discrete optimization problem with some property constraints as follows.
Find topological variables , where is the objective function, is the physical or geometric parameter of the th element used in the finite analysis, and are the th constraint response and its upper bound, respectively, is the total number of constraints, is the th independent topological design variable, is the independent topological design vector, and the total number of design variables is . In fact, may be regarded as function with as and as .
Due to the large dimensionality of the design space, this discrete problem is often relaxed to nonlinear optimization problems with continuous design variables instead of the binary design variables. Several methods have been proposed for topology optimization based on the physical or geometric values such as the variable element thickness [43, 44], crosssectional area [45], or pseudodensity as the design variables, that is, where , , and are the th element normalized density, thickness, and area, respectively, and is a penalization parameter in the SIMP method [16, 46].
To obtain the independent topological design variables by abstracting from the above physical parameters and geometrical parameters, the binary design variables in (1) can be relaxed to a new stepup function and be defined as (see Figure 1) where is the physical or geometric parameter of the th element used in the finite analysis, is the original and true physical or geometric parameter of the material used of the th element.
As an appropriate relaxation, is allowed to take all possible values between 0 and 1, and it is clear that is an ideal independent topological design variable which has clear physical meaning. However, the stepup function (5) is discontinuous in the point , but it is reasonable. In the conventional structural topology optimization, no matter how small, as long as the physical or geometric parameter of the th element is not equal to 0, then is equal to 1, and when is equal to 0, then the topological design variable suddenly becomes 0. In fact, the stepup function (5) describes the abovementioned discontinuous relation between and .
2.2. The Polish Function and the Continuous Approximation of the StepUp Function
Since the optimization problem (3) includes finite discontinuities that express the existence or nonexistence of elements, it is, therefore, not easily treated numerically and not solved by classical optimization algorithms based on continuous variables. In order to overcome this difficulty, we introduce the polish function to approximate the stepup function (5) such that becomes a continuous and differentiable function (see Figure 2); that is, where the following properties are satisfied.(1)Continuity and differentiability: is a continuous and differentiable function in .(2)Strict increasing monotonicity: is a strictly monotonically increasing function.(3)Convexity: is a convex function in .(4)Boundedness: , , and .(5)Approximation: or in .
Based on the concepts of the stepup function and polish function, can take values in the entire range instead of the discrete set and make the functional relationship between the topology design variables , and the various physical or geometric parameters become continuous and differentiable from discontinuous and nondifferentiable. This “discreterelaxationcontinuous” mapping process is called as polishing approximation mapping. In this paper, we take as the independent continuous topological design variables. Due to this extension of the design space, the existence of solutions is achieved (see Figure 7).
From the mechanical point of view, when the independent continuous topological design variables take intermediate values between 01, not only reflect the proximity to corresponding elements “have” and “no”, but also identify the size of the various physical values in the elements.
2.3. The Filter Mappings and the Weighting Filter Functions
To formulate the explicit continuous nonlinear topology optimization model for the optimization problem (3) and obtain the final topological graphs, we define the inverse mapping of the polishing approximation mapping as the filter mapping, which is denoted as following: that is,
Moreover, we define the inverse function of the polish function as the weighting filter function, where .
It is well known that the polish function and the weighting filter function may have a variety of options. Sui [38] suggested three kinds of simple filter functions as following (see Figure 3):
(a) power function
(b) composite exponential function
(c) modified sigmoid function
In fact, the inverse functions of the above three kinds of simple weighting filter functions (9), (10), and (11) satisfy continuity and differentiability, strict increasing monotonicity, convexity, boundedness, and approximation. However, the weighting filter function may be considered as the penalty function in the SIMP approach.
In the ICM method, the weighting filter function plays a very important role as follows.
(a) Let the inverse mapping of the stepup function in (5) denote as , which is a 01 binary multivalued function that cannot indicate the size of the physical or geometric parameter when the independent topological design variables take intermediate values between 01. To overcome this difficulty, we introduce the concept of weighting filter function which continuously approximates the inverse mapping of the stepup function , and it points out the proximity degree that closes to and 0.
(b) According to the ICM method, if or , then the variable weight and stiffness matrix of the th element, and are, respectively, recognized by the weighting filter functions as where and are the original weight and stiffness matrix of the th element for structural material, respectively.
Generally, when in (8) represents the different variable physical or geometric parameter, the weighting filter functions could have different forms; that is, where , , , , and are, respectively, the variable allowable stress, mass, volume, area, and thickness of the th element for structural material, and , , , , and are the original allowable stress, mass, volume, area, and thickness of the th element for structural material, respectively.
Moreover, we can give the solid isotropic material with penalization (SIMP) approach a new explanation from the view of the ICM method, that is, where and are the variable density and Young’s modulus of the th element for structural material and and are the original density and Young’s modulus of the th element, respectively.
(c) Based on the concept of the weighting filter function, the original objective and constraint functions are replaced by relatively simple, approximating functions which are fundamentally different from the penalization scheme and interpolation scheme in the SIMP method. Firstly, the ICM method takes the independent continuous topological design variable instead of the relative density in the SIMP method, which changes conventional continuous design variable taking the various physical or geometric parameters. Secondly, from the ICM method point of view, as long as the physical or geometric parameters change in the structural topology optimization process, the related weighting filter function (or interpolation scheme in terms of the SIMP method) should be considered as not limiting to the stiffness matrix. However, the weighting filter functions play the important roles as they eliminate grey scale transitions between solid and void regions.
(d) The weighting filter function can formulate the explicit continuous nonlinear topology optimization model and obtain the final topological graphs.
Let be the objective function of the th element used in the finite analysis and the original objective function of the th element for structural material, then we have that and the topology optimization model (3) could be approximated by the new continuous nonlinear topology optimization model; as follows.
Find topological variables
(e) The problems considered in this paper involve (von Mises) stresses, which can exhibit the socalled “singularity phenomena”, that is, due to the discontinuous nature of the stress when the topology design variables tend to zero in (14). This stress singularity phenomena has been dealt with by the smooth envelope functions method [47–49] or therelaxation approach [36, 50–56].
According to the ICM method, we can also introduce the concept of the variable allowable stress of the th element, which is recognized by the allowable stress weighting filter functions as (14) such that where is the original allowable stress of the th element for structural material and is the von Mises stress for the th element under the th load case. That is, the inequality (24) guarantees that , when tends to zero without violating the stress constraint.
In fact, (23) and (24) eliminate the abovementioned singularity phenomena, since a stress will be exactly zero in a void element, and they also remove the need for taking the complex value in the relaxation approach.
By exploiting the modeling of a simple microstructure [1, 51], they state that the similar variable allowable stress as stress threshold , where they apply a stress constraint for the SIMP model, that is, expressed as a constraint of the von Mises equivalent stress :
In the following, we prove that (23) and (24) can satisfy therelaxation conditions.
Without loss of generality, assuming the stress constraints in the continuum structure design [36, 51–56] as then we have that where , , and is an arbitrary small quantity in which its value determines the extent of relaxation and the inequality.
Moreover, the inequality (24) and (28) can imply that is,
It is well known that the inequality (30) is exactly the ordinarily formula in the relaxation approach.
In conclusion, the key idea of the ICM method is the independent continuous topological design variable, the polish and weighting filter mapping, and the simple discrete or inverse process.
3. An Alternative Strain Energy Method for the Topology Optimization Problem with Stress Constraints
In this section, an alternative strain energy method for the topology optimization design problem with stress constraints is proposed. The method is implemented in the topology optimization procedure to overcome the difficulty in stress sensitivity analyses.
3.1. Minimum Weight with Stress Constraints in Topology Optimization
For the general explicit continuous nonlinear topology optimization model (22), let be the von Mises stress for the th element under the th load case, the material density, and the original reference design domain. However, the integral on the volume of filled with material of the material density can be written as an integral on the total volume by multiplying by , and the objective function may be defined as Then, the original topology optimization problem with stress constraints can be formulated as follows.
Find topological variables ; where is the th independent continuous topological design variable following (8), is the independent continuous topological design vector, the total number of design variables is , and the total number of external loads is . As it is well known, a lower limit of the the th independent continuous topological design variable is imposed to avoid the singularity of the global stiffness matrix which is given as .
However, Sui et al. [42] and Tie et al. [57] study topology optimization of continuum structure with globalization of stress constraints using an efficient strain energy method.
In fact, most of papers referred to what was previously mentioned and the monographs ([1–3] and references therein) have been focused on global objectives, such as compliance, displacement, and frequency. It is fewer about the strain energy method in the structural topology optimization.
Since the essence of topology optimization lies in searching for the optimal path of transferring loads, the stress of the structural elements in the structural topology optimization has to be considered to obtain a reliable design. Clearly the stress of the structural elements is a local quantity, there are three difficulties for solving the local problem as follows: (a) the dimension number of the design variable is too extensive, and it is too difficult to be solved. However, every structural element in the structural topology optimization is just with the corresponding one design variable. Even for one stress at a point in each element region, it is only restricted as a stress constraint of the region; the whole structure with elements will be associated with stress when there is load cases. (b) Since the stress of the structural elements is highly nonlinear with respect to the design variables, the computational cost of stress sensitivity analysis is too expensive to be accepted. (c) The stress singularity phenomena must be also considered in the topology optimization problem mentioned above.
Thus it is necessary to introduce an alternative strain energy method to transform the local stress constraints into the strain energy constraints of the structural elements by means of the von Mises criteria in the theory of elastic failure.
3.2. The Derivation of an Alternative Strain Energy Method
It is well known that the von Mises stress at a point for deformation body is calculated by the formula as following where , , and are the principal stresses.
Let , and looking back upon the shear strain energy density from the von Mises strength theory in (33), then we have that where , are the Young’s modulus, Poisson’s ratio and is the allowable stress of the structural material, respectively.
Since the shear strain energy density is impossible to surpass the strain energy density, we have that where , , and are the strain energy density, the strain energy, and the volume of the th element under the th load case, respectively.
Note that the amplification factor can be computed in terms of socalled corrector terms and be given as in our work. Other values of could be considered, but they give similar results.
Let be the variable Young’s modulus of the th element for structural material, substituting (36) into (35), we have that where we remark that is defined as the variable allowable strain energy of the th element which is unique under multiload case and may be denoted by .
It is evident that the structure is safe, and the stress constraints are satisfied if the formulation (37) is established.
Moreover, substituting (14) and (20) into in (37), we have that where is defined as the original allowable strain energy of the th element which is also unique under multiload case.
Thus, we can modify (32) to a new formulation according to (37) such that the strain energy constraintis in (37) instead of the stress constraints in (32) as following.
Find topological variables;
By the properties of the parabolic aggregation function (see the next section), we have that
Substituting (40) into (39), the optimization model (32) is equivalent with the following when the parameter is enough big.
Find topological variables ;
4. The Parabolic Aggregation Function and the Global Strain Energy Approach
In this section, we present a new strategy to deal with topology optimization of continuum in (39). However, the above strain energybased approach produces highly nonlinear objective function and constraints so as to give rise to a complicated problem. That is, in finiteelementbased topology optimization, the number of strain energy is very large, and it is necessary to take into account that this approach may require large computing resources, and sensitivity computation is prohibitively costly.
A way out for overcoming this difficulty is to apply an aggregation function to deal with the large number strain energy which is frequently used in the topology optimization with stress constraints [52, 53, 56, 58–60]. Now we should explore a new aggregation function which differs from the common norm and KS function [61] and is very suitable for the topology optimization of continuum structures with stress or strain energy inequality constraints.
The proposed aggregation function that aggregates all the elemental strain energy into single global strain energy can be formulated as where is a KSlike global function and designated as the parabolic aggregation function ([62]; see Figure 4) at the same time we call KS function as the linear aggregation function. Indeed, the parameter does not depend on the strain energy, and the exponent of the parabolic aggregation function becomes dimensionless.
A simple mathematical proof for the global property of the parabolic aggregation function will be given here later. Moreover, in the context we denote that as the variable global allowable strain energy.
Theorem 1. Let , if the following conditions are satisfied: (1), ;(2), .And let ; then one has that
(a)
(b)
Proof. (a) If , then we have that
For
Let . Since , , then we have that
that is,
Taking the limit in (49) as , we get that
(b) Now, we prove that . Consider the following:
Since , then the function
is descent for variable .
Let , then .
For an increasing ordered series of numbers
we have that
Taking the limit in (55) as , then the result (b) follows.
Now, we compare the linear and parabolic aggregation function by the following numerical Example A. In the calculation, we should gradually increase until the numerical overflow in order to determine the range of , so that the linear and parabolic aggregation function approximates well the maximum envelope function .
Example A.
It can be seen from Figure 5 that the optimal solution is and in Example A.
The comparison results for Example A of the linear and parabolic aggregatation function could be seen in Table 1, where the linear aggregatation function is and the parabolic aggregatation function is .

Figure 5 and Table 1 show that the number of iterations is 17 and the parameter when the satisfactory solution is available for the linear aggregate function. At the same time, for the parabolic aggregate function, the number of iterations is also 17, and the computation is not increased when the parameter , and the satisfactory solution is available. But the parabolic aggregate function can better approximate to the maximum envelope function and results in the significant reduction of the number of structural reanalyses so as to make a smaller computational cost. The latter example will illustrate this point in Section 6.
5. Solution Strategy Based on the ICM Method and the Parabolic Aggregate Function
In this section, the optimization problem (41) can be solved by many continuoustype optimization algorithm. However, the simplest choice of the numerical treatment is still the serial Lagrange multiplier method (SLMM) which could efficiently deal with the structural reanalysis of the nonlinear topology optimization problems and obtain an explicit expression of the independent continuous topological design variables.
Now, we will give the process of the structural reanalysis based on the ICM method and the serial Lagrange multiplier method (SLMM).
For convenience, let , , and .
The element strain energy satisfies the following relationship as where and are the variable elemental stiffness and the elemental node force vector, respectively.
Substituting the weighting filter function in (13) into (57), then we have that
Assume in the th structural analysis that we have that then in the th structural analysis, and we have that
In the context, we define the active set as , the lower passive set as , and the upper passive set as . Substituting (59) and (60) into in (41), then in the th structural analysis we have that
Let , , and ; then (58) is equivalent with
In order to achieve the constraint in (59) explicitly, we demand the firstorder partial derivatives of on the design variables:
Then, the firstorder Taylor expansion formula of is Substituting the intermediate variable into (64), then we have that where
Hence, the approximate model of (41) can be expressed as follows.
Find topology variables ;
Setting aside consideration of interval constraint , then the augmented Lagrange function of (67) can be expressed as
Taking extreme conditions for the saddle point in (68),
By (69) we can have that
Substituting (71) into (70), we can have that
Substituting (72) into (71), we can have that
With consideration on interval constraint , we can have that
Record and update the active set and the passive set and ; then return to (73) to calculate , this process is repeated until the same set of active and passive sets does not change and meets the convergence criteria for iteration: where and are the total weight of the structure of previous and current iterations, respectively, and is the convergence precision.
6. Numerical Examples
In this section, we present three numerical results to demonstrate the capabilities of the proposed methods. The structural optimization problem in all examples is to solve the optimization model (67) so as to obtain the optimal topologies of the original topology optimization problem with stress constraints in (32) and its equivalent optimization model with strain energy constraints in (41). Actually, all the examples are twodimensional. However, the threedimensional problems would work as well in principle.
6.1. The Short Cantilever Beam
The first example is the short cantilever beam shown in Figure 6(a), with a clamped left hand side, and a vertical load applied at the middle of the right hand side. The rectangular domain (0.1 m thick) containing the structure is discretized into 60 × 20 meshes by using fournode membrane element. In the model, the density of material is used to be kg/m^{3}, the Poisson’s radio is , and the elastic module of material is N/m^{2}. In order to avoid stress concentration, the load N is located at the three nodes in the middle of the right side. The allowable stress is 100 N, and the convergence precision is limited to be 0.001.
(a)
(b)
(a)
(b)
(c)
Thus, the optimal topology structure is shown in Figure 6(b), and the historical curves of the elemental maximum stress, the structural strain energy, the elemental maximum strain energy, and the structural weight are shown in Figures 7, 8, 9, and 10, respectively. The optimal design was obtained in 69 iterations, where the number of elements reserved is 383 (see Table 2), the total structure strain energy is 0.0290543 J, the average elemental strain energy of the element reserved is 7.58597 E5 J, and the elemental maximum strain energy is 0.000246914 J. From strength considerations in the optimal topology structure, the maximum stress of the structural elements is at the node 609, and its value is 81.9 N which meets the strength requirements, and the total weight of the optimal structure is 2.4827 kg, in which the parameter of the parabolic aggregatation function is .

6.2. The MBBBeam
The second example is the MBBbeam shown in Figure 11 (cf. [63]), in which material constants are pa, , and kg/m^{3}, where a vertically force N is applied at the center point of the upper side, and the bottomleft corner is supported as a roller, while the bottomright corner is a fixed support. The design domain (1 m thick) is discretized into 180 × 30 meshes by Q4 elements. The allowable stress is 1.0 kN, and the convergence precision is limited to be 0.001.
Thus, the optimal topology structure is shown in Figure 12, and the historical curves of the elemental maximum stress, the structural strain energy, the elemental maximum strain energy, and the structural weight are shown in Figures 13, 14, 15, and 16, respectively. The optimal design was obtained in 46 iterations, where the number of elements reserved is 2550 (see Table 3), the total structure strain energy is 0.0519843 J, the average elemental strain energy of the element reserved is 2.0386E5 J, and the elemental maximum strain energy is 21.446 E5 J. From strength considerations in the optimal topology structure, the maximum stress of the structural elements is at the node 5520, and its value is 975 N which meets the strength requirements, and the total weight of the optimal structure is 88.2662 kg, in which the parameter of the parabolic aggregatation function is .

(a)
(b)
(c)
6.3. The LShape Beam
The third example is the Lshape beam shown in Figure 17 (cf. [63]), which is clamped on its top boundary and loaded on the mid 1/3 of the right side by the uniformly distributed load . The material constants are pa, , kg/m^{3}, and m, and the uniformly distributed load is N. The Lshape domain (0.027 m thick) containing the structure is discretized into 18 × 18 × 3 meshes by using fournode membrane element with 972 elements. The allowable stress is 70 N, and the convergence precision is limited to be 0.001.
Thus, the optimal topology structure is shown in Figure 18, and the historical curves of the elemental maximum stress, the structural strain energy, the elemental maximum strain energy, and the structural weight are shown in Figures 19, 20, 21, and 22, respectively. The optimal design was obtained in 32 iterations, where the number of elements reserved is 586 (see Table 4), the total structure strain energy is 0.0221408 J, the average elemental strain energy of the element reserved is 3.7783E5 J, and the elemental maximum strain energy is 0.000343573 J. From strength considerations in the optimal topology structure, the maximum stress of the structural elements is at the node 686, and its value is 160 N which meets the strength requirements, and the total weight of the optimal structure is 1762.9 kg, in which the parameter of the parabolic aggregatation function is .

(a)
(b)
(c)
7. Conclusions
This paper develops three novel methods for solving stressbased topology optimization problems, which are the independentcontinuousmapping (ICM) method, an alternative strain energy method, and the parabolic aggregation function, that is, different from the two common global functions such asnorm and KS function. The numerical examples demonstrate that the proposed methods effectively remove the stress concentrations and generate blackandwhite designs for practically sized problems.
Acknowledgments
This paper is supported by the National Natural Science Foundation of China (10872012, 70901010, and 11172013), the Foundation of National Key Laboratory for Structural Analysis of Industrial Equipment (Dalian University of Technology) (GZ1008), and the Postdoctoral Science Foundation of Beijing University of Technology (X0001015200803).
References
 M. P. Bendsøe and O. Sigmund, Topology Optimization: Theory, Methods and Applications, Springer, Berlin, Germany, 2003.
 A. Henrot and M. Pierre, “Variation et optimisation de formes,” in Mathématiques et Applications, vol. 48, Springer, Heidelberg, The Netherlands, 2005. View at: Google Scholar
 G. Allaire, “Conception optimale de structures,” in Mathématiques et Applications, vol. 58, Springer, Berlin, Germany, 2007. View at: Google Scholar
 M. P. Bendsøe and N. Kikuchi, “Generating optimal topologies in structural design using a homogenization method,” Computer Methods in Applied Mechanics and Engineering, vol. 71, no. 2, pp. 197–224, 1988. View at: Google Scholar
 G. I. N. Rozvany, Structural Design via Optimality Criteria, Kluwer Academic Publishers, Dordrecht, The Netherlands, 1988.
 M. P. Bendsøe, “Optimal shape design as a material distribution problem,” Structural Optimization, vol. 1, no. 4, pp. 193–202, 1989. View at: Publisher Site  Google Scholar
 M. P. Bendsoe, Optimization of Structural Topology, Shape and Material, Springer, Berlin, Germany, 1997.
 K. Suzuki and N. Kikuchi, “A homogenization method for shape and topology optimization,” Computer Methods in Applied Mechanics and Engineering, vol. 93, no. 3, pp. 291–318, 1991. View at: Google Scholar
 A. R. Diaz and N. Kikuchi, “Solutions to shape and topology eigenvalue optimization problems using a homogenization method,” International Journal for Numerical Methods in Engineering, vol. 35, pp. 1487–1502, 1992. View at: Google Scholar
 M. P. Bendsøe and R. B. Haber, “The Michell layout problem as a low volume fraction limit of the perforated plate topology optimization problem: an asymptotic study,” Structural Optimization, vol. 6, no. 4, pp. 263–267, 1993. View at: Publisher Site  Google Scholar
 G. Allaire and R. V. Kohn, “Optimal design for minimum weight and compliance in plane stress using extremal microstructures,” European Journal of Mechanics A, vol. 12, no. 6, pp. 839–878, 1993. View at: Google Scholar
 G. Allaire, E. Bonnetier, G. Francfort, and F. Jouve, “Shape optimization by the homogenization method,” Numerische Mathematik, vol. 76, no. 1, pp. 27–68, 1997. View at: Google Scholar
 G. Allaire, F. Jouve, and H. Maillot, “Topology optimization for minimum stress design with the homogenization method,” Structural and Multidisciplinary Optimization, vol. 28, no. 23, pp. 87–98, 2004. View at: Publisher Site  Google Scholar
 M. Zhou and G. I. N. Rozvany, “The COC algorithm—part II: topological, geometrical and generalized shape optimization,” Computer Methods in Applied Mechanics and Engineering, vol. 89, no. 13, pp. 309–336, 1991. View at: Google Scholar
 H. P. Mlejnek, “Some aspects of the genesis of structures,” Structural Optimization, vol. 5, no. 12, pp. 64–69, 1992. View at: Publisher Site  Google Scholar
 M. P. Bendsøe and O. Sigmund, “Material interpolation schemes in topology optimization,” Archive of Applied Mechanics, vol. 69, no. 910, pp. 635–654, 1999. View at: Google Scholar
 H. A. Eschenauer and N. Olhoff, “Topology optimization of continuum structures: a review,” Applied Mechanics Reviews, vol. 54, no. 4, pp. 331–389, 2001. View at: Google Scholar
 Z. Gaspar, J. Logo, and G. I. N. Rozvany, “Aim, scope, methods, history and unified terminology of computer aided topology optimization in structural mechanisms,” Structural and Multidisciplinary Optimization, vol. 21, no. 2, pp. 90–108, 2001. View at: Google Scholar
 G. I. N. Rozvany, “A critical review of established methods of structural topology optimization,” Structural and Multidisciplinary Optimization, vol. 37, no. 3, pp. 217–237, 2009. View at: Publisher Site  Google Scholar
 M. P. Bendsøe, E. Lund, N. Olhoff, and O. Sigmund, “Topology optimization—broadening the areas of application,” Control and Cybernetics, vol. 34, no. 1, pp. 7–36, 2005. View at: Google Scholar
 O. Sigmund, “Design of multiphysics actuators using topology optimization—part I: onematerial structures,” Computer Methods in Applied Mechanics and Engineering, vol. 190, no. 4950, pp. 6577–6604, 2001. View at: Publisher Site  Google Scholar
 J. Petersson and O. Sigmund, “Slope constrained topology optimization,” International Journal for Numerical Methods in Engineering, vol. 41, no. 8, pp. 1417–1434, 1998. View at: Google Scholar
 Y. M. Xie and G. P. Steven, “A simple evolutionary procedure for structural optimization,” Computers and Structures, vol. 49, no. 5, pp. 885–896, 1993. View at: Google Scholar
 Y. M. Xie and G. P. Steven, Evolutionary Structural Optimization, Springer, Berlin, Germany, 1997.
 O. M. Querin, G. P. Steven, and Y. M. Xie, “Evolutionary structural optimisation (ESO) using a bidirectional algorithm,” Engineering Computations, vol. 15, no. 8, pp. 1031–1048, 1998. View at: Google Scholar
 X. Y. Yang, Y. M. Xie, G. P. Steven, and O. M. Querin, “Bidirectional evolutionary method for stiffness optimization,” AIAA Journal, vol. 37, no. 11, pp. 1483–1488, 1999. View at: Google Scholar
 X. Huang and Y. M. Xie, “Bidirectional evolutionary topology optimization of continuum structures with one or multiple materials,” Computational Mechanics, vol. 43, no. 3, pp. 393–401, 2009. View at: Publisher Site  Google Scholar
 X. Huang and Y. M. Xie, “Convergent and meshindependent solutions for the bidirectional evolutionary structural optimization method,” Finite Elements in Analysis and Design, vol. 43, no. 14, pp. 1039–1049, 2007. View at: Publisher Site  Google Scholar
 J. A. Sethian and A. Wiegmann, “Structural boundary design via level set and immersed interface methods,” Journal of Computational Physics, vol. 163, no. 2, pp. 489–528, 2000. View at: Publisher Site  Google Scholar
 S. J. Osher and F. Santosa, “Level setmethods for optimization problems involving geometry and constraints—I. Frequencies of a twodensity inhomogeneous drum,” Journal of Computational Physics, vol. 171, no. 1, pp. 272–288, 2001. View at: Publisher Site  Google Scholar
 S. Osher and R. Fedkiw, Level Set Methods and Dynamic Implicit Surfaces, Springer, Berlin, Germany, 2003.
 M. Y. Wang, X. Wang, and D. Guo, “A level set method for structural topology optimization,” Computer Methods in Applied Mechanics and Engineering, vol. 192, no. 12, pp. 227–246, 2003. View at: Publisher Site  Google Scholar
 Z. Luo, L. Tong, and M. Y. Wang, “Design of distributed compliant micromechanisms with an implicit free boundary representation,” Structural and Multidisciplinary Optimization, vol. 36, no. 6, pp. 607–621, 2008. View at: Publisher Site  Google Scholar
 Z. Luo, M. Y. Wang, S. Wang, and P. Wei, “A level setbased parameterization method for structural shape and topology optimization,” International Journal for Numerical Methods in Engineering, vol. 76, no. 1, pp. 1–26, 2008. View at: Publisher Site  Google Scholar
 M. Stolpe and K. Svanberg, “Modelling topology optimization problems as linear mixed 01 programs,” International Journal for Numerical Methods in Engineering, vol. 57, no. 5, pp. 723–739, 2003. View at: Publisher Site  Google Scholar
 K. Svanberg and M. Werme, “Sequential integer programming methods for stress constrained topology optimization,” Structural and Multidisciplinary Optimization, vol. 34, no. 4, pp. 277–299, 2007. View at: Publisher Site  Google Scholar
 A. Fügenschuh and M. Fügenschuh, “Integer linear programming models for topology optimization in sheet metal design,” Mathematical Methods of Operations Research, vol. 68, pp. 313–331, 2008. View at: Google Scholar
 Y. K. Sui, Modelling, Transformation and Optimization—New Developments of Structural Synthesis Method, Dalian University of Technology Press, 1996, (Chinese).
 Y. Sui and D. Yang, “New method for structural topological optimization based on the concept of independent continuous variables and smooth model,” Acta Mechanica Sinica, vol. 14, no. 2, pp. 179–185, 1998. View at: Google Scholar
 Y. K. Sui, “ICM method of topological optimization for truss, frame and continuum structure,” in Proceedings of the WCSMO4, Dalian, China, 2001. View at: Google Scholar
 Y. K. Sui, L. C. Long, and X. C. Ren, “Topological optimization of frame structures with stress and displacement constraints undermultiloading cases,” in Proceedings of the WCSMO4, Dalian, China, 2001. View at: Google Scholar
 Y.K. Sui, X.R. Peng, H.L. Ye, J.L. Feng, and L.C. Long, “Topology optimization of continuum structure with globalization of stress constraints,” Journal of Beijing University of Technology, vol. 32, supplement, pp. 1–8, 2006. View at: Google Scholar
 L. H. Tenek and I. Hagiwara, “Static and vibrational shape and topology optimization using homogenization and mathematical programming,” Computer Methods in Applied Mechanics and Engineering, vol. 109, no. 12, pp. 143–154, 1993. View at: Google Scholar
 G.W. Jang, Y.M. Choi, and G.J. Choi, “Discrete thickness optimization of an automobile body by using the continuousvariablebased method,” Journal of Mechanical Science and Technology, vol. 22, no. 1, pp. 41–49, 2008. View at: Publisher Site  Google Scholar
 A. Takezawa, S. Nishiwaki, K. Izui, and M. Yoshimura, “Structural optimization based on topology optimization techniques using frame elements considering crosssectional properties,” Structural and Multidisciplinary Optimization, vol. 34, no. 1, pp. 41–60, 2007. View at: Publisher Site  Google Scholar
 R. J. Yang and A. I. Chahande, “Automotive applications of topology optimization,” Structural Optimization, vol. 9, no. 34, pp. 245–249, 1995. View at: Publisher Site  Google Scholar
 G. I. N. Rozvany, M. Zhou, and T. Birker, “Generalized shape optimization without homogenization,” Structural Optimization, vol. 4, no. 34, pp. 250–254, 1992. View at: Google Scholar
 G. I. N. Rozvany, U. Kirsch, M.P. Bendsøe, and O. Sigmund, “Layout optimization of structures,” Applied Mechanics Reviews, vol. 48, no. 2, pp. 41–119, 1995. View at: Google Scholar
 G. I. N. Rozvany, “Difficulties in truss topology optimization with stress, local buckling and system stability constraints,” Structural Optimization, vol. 11, no. 34, pp. 213–217, 1996. View at: Google Scholar
 G. D. Cheng and X. Guo, “εrelaxed approach in structural topology optimization,” Structural Optimization, vol. 13, no. 4, pp. 258–266, 1997. View at: Google Scholar
 P. Duysinx and M. P. Bendsøe, “Topology optimization of continuum structures with local stress constraints,” International Journal for Numerical Methods in Engineering, vol. 43, no. 8, pp. 1453–1478, 1998. View at: Google Scholar
 P. Duysinx and O. Sigmund, “New developments in handling stress constraints in optimal material distribution,” in Proceedings of the 7th AIAA/USAF/NASA/ISSMO Symposium on Multidisciplinary Design Optimization, pp. 1501–1509, American Institute of Aeronautics and Astronautics, Saint Louis, Mich, USA, 1988. View at: Google Scholar
 J. T. Pereira, E. A. Fancello, and C. S. Barcellos, “Topology optimization of continuum structures with material failure constraints,” Structural and Multidisciplinary Optimization, vol. 26, no. 12, pp. 50–66, 2004. View at: Publisher Site  Google Scholar
 M. Bruggi, “On an alternative approach to stress constraints relaxation in topology optimization,” Structural and Multidisciplinary Optimization, vol. 36, no. 2, pp. 125–141, 2008. View at: Publisher Site  Google Scholar
 J. París, F. Navarrina, I. Colominas, and M. Casteleiro, “Topology optimization of continuum structures with local and global stress constraints,” Structural and Multidisciplinary Optimization, vol. 39, no. 4, pp. 419–437, 2009. View at: Publisher Site  Google Scholar
 J. París, F. Navarrina, I. Colominas, and M. Casteleiro, “Block aggregation of stress constraints in topology optimization of structures,” Advances in Engineering Software, vol. 41, no. 3, pp. 433–441, 2010. View at: Publisher Site  Google Scholar
 J. Tie, Y.K. Sui, and D.H. Suan, “On solving the minmax problem of the stress of the Structural elements in the structural topology optimization,” in Proceedings of the IEEE International Conference on Advanced Computer Control (ICACC '10), pp. 399–402, March 2010. View at: Publisher Site  Google Scholar
 R. J. Yang and C. J. Chen, “Stressbased topology optimization,” Structural Optimization, vol. 12, no. 23, pp. 98–105, 1996. View at: Google Scholar
 J. R. R. A. Martins and N. M. K. Poon, “On structural optimization using constraint aggregation,” in Proceedings of the 6th World Congress on Structural and Multidisciplinary Optimization (WCSM '05), Rio de Janeiro, Brazil, June 2005. View at: Google Scholar
 G. Y. Qiu and X. S. Li, “A note on the derivation of global stress constraints,” Structural and Multidisciplinary Optimization, vol. 40, no. 1–6, pp. 625–628, 2010. View at: Publisher Site  Google Scholar
 G. Kreisselmeier and R. Steinhauser, “Systematic control design by optimizing a vector performance index,” in Proceedings of the International Federation of Active Controls Symposium on ComputerAided Design of Control Systems, pp. 113–117, Zurich, Switzerland, 1979. View at: Google Scholar
 T. Jun and Y.K. Sui, “The parabolic KS function for the structural topology optimization,” in Proceedings of the 2nd International Workshop on Computer Science and Engineering (WCSE '09), pp. 385–387, October 2009. View at: Publisher Site  Google Scholar
 A. A. Groenwold and L. F. P. Etman, “A simple heuristic for grayscale suppression in optimality criterionbased topology optimization,” Structural and Multidisciplinary Optimization, vol. 39, no. 2, pp. 217–225, 2009. View at: Publisher Site  Google Scholar
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