Crushing analysis and multi-objective optimization design for bionic thin-walled structure

ABSTRACTDue to extraordinary energy absorption capacity as well as light weight, functionally graded foam-filled cellular structure (FGFCS) has gained considerable attention. Using nonlinear finite element method through LS-DYNA, this work studied the bending crashworthiness of nine FGFCSs with different cross-sectional configurations. The results demonstrate that the bending crashworthiness of the FGFCSs is significantly affected by the design parameter of the graded functional parameter q. Thus, in order to find the optimal gradient exponential parameter, the FGFCSs with different cross sections were optimised using the radial basis function metamodel and the Non-dominated Sorting Genetic Algorithm II. Meanwhile, the corresponding uniform foam-filled cellular structures (UFCSs) with the same weight as above FGFCSs were also optimised in our study. In the optimisation process, the aim is to achieve maximum value of specific energy absorption and minimum value of peak crushing force. By comparing the Pareto fronts obtained by multi-objective optimisation, it can be found that FGFCSs have better crashworthiness than the corresponding UFCSs in most considered cases. Thus, the optimal design of FGFCS has exactly an excellent energy absorption capacity under lateral impact and can be used in the future vehicle body.

Crashworthiness design of functionally graded foam-filled multi-cell thin-walled structures

Nowadays, thin-walled foam-filled structures have already been excessively used in automobile industry due to the superior energy absorption capacity and relatively light weight. The components in vehicle probably subject to lateral impact at any position in practice; however, most of the previous literature focused only on the bending behavior of structures under lateral impact at the mid-span. In this study, a hybrid structure of the structural epoxy foam Terocore ® and two cylindrical tubes is comprehensively investigated under various lateral impact positions. The finite element model of the hybrid structure is established and then validated by the experimental results. From a numerical study, several design parameters, including the thicknesses of outer and inner tubes, the diameters of inner tubes, and the foam densities, are explored to exhibit great effects on the bending resistance of the hybrid structure. To find the optimal designs of the hybrid structure under different load cases, a system methodology, which is constructed by optimal Latin hypercube sampling, radial basis function model and multi-objective particle swarm optimization algorithm, is implemented. Compared with the original design, the optimization designs of different load cases perform better bending resistance, namely, higher specific energy absorption and lower peak crushing force. Therefore, the optimal hybrid structure can be considered as a practical candidate for energy absorbing under lateral impact.

In data center companies, cloud computing can host multiple types of heterogeneous virtual machines (VMs) and provide many features, including flexibility, security, support, and even better maintenance than traditional centers. However, some issues need to be considered, such as the optimization of energy usage, utilization of resources, reduction of time consumption, and optimization of virtual machine placement. Therefore, this paper proposes an alternative multiobjective optimization (MOP) approach that combines the salp swarm and sine-cosine algorithms (MOSSASCA) to determine a suitable solution for virtual machine placement (VMP). The objectives of the proposed MOSSASCA are to maximize mean time before a host shutdown (MTBHS), to reduce power consumption, and to minimize service level agreement violations (SLAVs). The proposed method improves the salp swarm and the sine-cosine algorithms using an MOP technique. The SCA works by using a local search approach to improve the performance of traditional SSA by avoiding trapping in a local optimal solution and by increasing convergence speed. To evaluate the quality of MOSSASCA, we perform a series of experiments using different numbers of VMs and physical machines. The results of MOSSASCA are compared with well-known methods, including the nondominated sorting genetic algorithm (NSGA-II), multiobjective particle swarm optimization (MOPSO), a multiobjective evolutionary algorithm with decomposition (MOEAD), and a multiobjective sine-cosine algorithm (MOSCA). The results reveal that MOSSASCA outperforms the compared methods in terms of solving MOP problems and achieving the three objectives. Compared with the other methods, MOSSASCA exhibits a better ability to reduce power consumption and SLAVs while increasing MTBHS. The main differences in terms of power consumption between the MOSCA, MOPSO, MOEAD, and NSGA-II and the MOSSASCA are 0.53, 1.31, 1.36, and 1.44, respectively. Additionally, the MOSSASCA has higher MTBHS value than MOSCA, MOPSO, MOEAD, and NSGA-II by 362.49, 274.70, 585.73 and 672.94, respectively, and the proposed method has lower SLAV values than MOPSO, MOEAD, and NSGA-II by 0.41, 0.28, and 1.27, respectively.

Recently, customers are demanding for hot rolled strip products to have tight oxide scales on the surfaces. Therefore, high finishing rolling temperature, low coiling temperature and fast finishing rolling speed have to be used to obtain tight oxide scale, which is different from conventional controlled rolling. In order to ensure the mechanical properties at the same time, a framework consisting of the Bayesian neural network and multi-objective particle swarm optimization has been established to determine the optimal hot strip rolling parameters. Due to excellent generalization ability, the Bayesian neural network was employed to develop the model for the prediction of mechanical properties of hot rolled automotive beam steels. The accuracy between the measured and predicted values was within ±30 MPa and ±4% for strength and elongation, respectively, providing a reliable model for the optimal process design. By applying multi-objective particle swarm optimization, the optimized hot rolling process was obtained for the production of hot rolled automotive beam steel with “Tight Oxide Scale”. Industrial trials have been carried out, which showed good agreement with the optimized hot strip rolling processes. It has been theoretically and practically proven that the optimal process design framework can effectively locate the optimal processing window for hot strip rolling.

Airbag landing system has recently been used more and more extensively in industry for its excellent energy absorption capacity as well as extremely light weight. Firstly, we investigate a single-chamber airbag (SCA) under dynamic loading through experiment by using the Dynatup 9200 impact testing machine. Then, the corresponding non-linear finite element (FE) model of the SCA is established through LS-DYNA. From the comparison between the numerical results and the experimental results, we find that the FE model of the SCA is accurate enough for the engineering application. According to the FE modelling method of the SCA, the non-linear FE model a double-chamber airbag (DCA) landing system is established. Based on the FE numerical results, the polynomial function meta-models of the peak acceleration and the peak rebound velocity of the landing equipment of the DCA landing system are obtained by employing the error reduction ratio (ERR) structure-selection techniques. Finally, the DCA landing system is optimised on the basis of the meta-models by adopting the multi-objective particle swarm optimisation (MOPSO) algorithm to achieve minimum peak acceleration as well as minimum peak rebound velocity of the landing equipment.

Some achievements have been made in the study on mechanical properties of antler, but they have not been applied in engineering practice, especially in the study of thin-walled tubes similar to antler crashworthiness. In order to improve the crashworthiness and energy absorption of the thin-walled tube structures, a bionic thin-walled tube with the same inner diameter and equal gradient of outer diameter was designed based on the structural characteristics of antler bone and the principle of structural bionics. The finite element method was used to simulate the energy absorption characteristics of 75 kinds of bionic thin-walled tube structures under the oblique impacts with the impact angles of 10°, 20° and 30°. The polynomial regression element model and multi-objective particle swarm optimization algorithm were used to optimize, and the Pareto front optimization principle was used to obtain the optimal allocation scheme of each target. The minimum distance selection method was used in optimization analysis to obtain the optimal structural design parameters of each scheme. The optimization method used in this study can provide reference for the follow-up research on the crashworthiness of thin-walled tubes, and the optimal structure of bionic thin-walled tubes can provide reference for practical engineering application. The results show that when only considering a single impact angle, the optimal number of biomimetic layers n is 6, and the parameter combination of maximum wall thickness and thickness gradient tmax-a is 2.84 mm-0.38 mm, 2.89 mm-0.29 mm, 2.91 mm-0.34 mm, respectively under 10°, 20° and 30° impact angles. Considering various impact angle weight factors and different configuration schemes, the optimal number of biomimetic layers n is 6, and the parameter combination of maximum wall thickness and thickness gradient tmax-a is 2.95 mm-0.28 mm, 2.92 mm-0.30 mm and 2.85 mm-0.33 mm, respectively.

An integrated optimization strategy based on Kriging model and multi-objective particle swarm optimization (PSO) algorithm was constructed. As a new surrogate model technology, Kriging model has better fitting precision for nonlinear problem. The Kriging model was adopted to replace computer aided engineering (CAE) simulation as fitness function of multi-objective PSO algorithm, and the computation cost can be reduced greatly. By introducing multi-objective handling mechanism of crowding distance and mutation operator to multi-objective PSO algorithm, the entire Pareto front can be approximated better. It is shown that the multi-objective optimization strategy can get higher solving accuracy and computation efficiency under small sample.

This paper addresses a multi-objective approach for simultaneous dynamic expansion planning of conventional sub-transmission grid and Regional Virtual Power Plant (RVPP). In such a feed-in tariff electricity market, it will assist RVVP’s stakeholders in deciding whether or not they should invest in new equipment’s installation. A Multi-Objective Particle Swarm Optimization (MOPSO) algorithm as a heuristic optimization method is proposed to eliminate the conventional centralized planning, which has led to competition between Regional Electric Company (REC) and RVPP for power delivery in sub-transmission system. Two objective functions are considered for simultaneous expansion planning of these two systems. The first one takes the minimum cost of the REC, as the sub-transmission grid operator, into account while the other one considers profit maximization for RVPP. To achieve the goals, MOPSO algorithm is employed to find the best expansion of REC and best location and capacity of RVPP’s resources. Having solved the proposed multi-objective optimization problem, a Pareto front is determined to show the trade-off between REC and RVPPs' contributions in joint optimal expansion planning of conventional sub-transmission grid and the RVPP. To demonstrate the applicability and effectiveness of the proposed approach, a realistic sub-transmission system in Guilan Province, Iran is used as a test system, and the results are evaluated accordingly.

In the last decade, multiobjective Particle Swarm Optimization (MOPSO) has been observed as one of the most powerful optimization algorithms in solving Multiobjective Optimization Problems (MOPs). Nowadays, it is becoming increasingly clear that MOPSO can handle complex MOPs based on the competitive-cooperative framework. The goal of this paper is to provide a comprehensive review of MOPSO from the basic principles to hybrid evolutionary strategies. To offer the readers insights on the prominent developments of MOPSO, the key parameters on the convergence and diversity performance of MOPSO were analyzed to reflect the influence on the searching performance of particles. Then, the main advanced MOPSO methods were discussed, as well as the theoretical analysis of multiobjective optimization performance metrics. Even though some hybrid MOPSO methods show promising multiobjective optimization performance, there is much room left for researchers to improve further, in particular in terms of engineering applications. As a result, further in-depth studies are required. This paper should motivate evolutionary computation researchers to pay more attention to this practical yet challenging area.

The shortage of current method which is used during the optimal design of the uniform magnetic field is that it only takes how to obtain the optimum dimensions of coil system into account without taking much consideration to the practicability of the optimum system(stability of coil system). Considering the current method's shortage, a kind of method with the purpose to enhance the uniformity of the magnetic field and stability is proposed in this paper. Therefore, a multi-objective optimization model of the coils dimensions optimal design is built firstly with the coils' endpoint co-ordinates as the optimized parameters and uniformity of coils system and stability of the system as optimized target. Then the Pareto solution set of the optimization problem is obtained by a kind of multi-objective particle swarm optimization (MOPSO). Last, the method's effectiveness is verified by a certain example and the results are analyzed. The result show that the method can enhance both the uniform magnetic field and stability of magnetic field at the same time, and it can provide a set of different dimensions for the designer's choices.

In this paper, a multi-objective particle swarm optimization algorithm based on the relative distance strategy is proposed to realize hydrodynamic coefficient identification, which can improve the accuracy of parameter identification without determining the weight manually. Firstly, single-objective particle swarm optimization (PSO) is used to identify the parameters of the model with a low coupling degree. Then, the parameters of the strongly coupled sway and yaw motion model are regarded as multi-objective, and a set of unbiased Pareto optimal solutions are obtained by the multi-objective particle swarm optimization (MOPSO) algorithm. Then the distance function of the Pareto solution is constructed to determine the parametric solution of the model. Finally, experimental identification is carried out by using the data set collected from the real ship. Experimental results show that the accuracy of model parameters obtained by this identification method is about 91.6%, and there is no need to set the weighting coefficient.

Heuristic-based multi-objective optimization design for viscous dampers in RC frame structures considering soil-structure interaction

A novel bio-inspired fractal-like multi-cell tube (FMT) was developed based on the Fibonacci spiral structure in creatures. A numerical study on FMTs with different sectional configurations was conducted under axial crushing using the nonlinear finite element method. Three criteria used to evaluate the crashworthiness of the FMTs: initial peak crush force ( Fp), specific energy absorption ( Es), and crush force efficiency ( Cf). The effects of different numbers of helical ribs N and wall thickness t on the crashworthiness and deformation mode of the FMTs were analyzed. Secondly, an analytic hierarchy process (AHP) method and a complex proportional assessment (COPRAS) method were applied to select the best possible sectional configuration. According to this method, three types of FMT with one, three, and five ribs (FMT1, FMT3, and FMT5) were determined to have a superior sectional configuration. Finally, a metamodel-based multi-objective optimization method based on a polynomial regression metamodel and multi-objective particle optimization (MOPSO) algorithm were employed to optimize the structural parameters of the FMT1, FMT3 and FMT5 under six designed cases, including two criteria cases and three criteria cases, where t and basic diameter D1 of the Fibonacci helical were considered the design variables. The multi-objective optimization results indicated that the optimal D1 does not exceeding 15 mm, the optimal t ranges from 0.29 to 0.72 mm under three criteria cases. Further research pertaining to the crashworthiness analysis of FMTs under oblique crushing should be conducted.

A multi-objective particle swarm optimization (MOPSO) approach for multi-objective economic load dispatch problem in power system is presented in this paper. The economic load dispatch problem is a non-linear constrained multi-objective optimization problem. The proposed MOPSO approach handles the problem as a multi-objective problem with competing and non-commensurable fuel cost, emission and system loss objectives and has a diversity-preserving mechanism using an external memory (call “repository”) and a geographically-based approach to find widely different Pareto-optimal solutions. In addition, fuzzy set theory is employed to extract the best compromise solution. Several optimization runs of the proposed MOPSO approach were carried out on the standard IEEE 30-bus test system. The results revealed the capabilities of the proposed MOPSO approach to generate well-distributed Pareto-optimal non-dominated solutions of multi-objective economic load dispatch. Comparison with Multi-objective Evolutionary Algorithm (MOEA) showed the superiority of the proposed MOPSO approach and confirmed its potential for solving multi-objective economic load dispatch.