Size Optimization Method Research Articles

Aircraft joint structure lightweight design

PurposeThe purpose of this article is to carry out a lightweight design of the joint structure according to the service condition of the joint.Design/methodology/approachThe finite element analysis techniques are used along with the variable density structure topology optimization method, the multi-island genetic algorithm for structural dimension optimization and the fatigue life analysis method.FindingsUtilizing the topology optimization method and size optimization method, the mass of the optimized model for the A100 material model is 9.67 kg. Compared to the pre-optimized model, the mass decreases by 8.23 kg, representing a weight reduction of 46.0% in the optimized model; the fatigue life of the aircraft joint is predicted to be a maximum of 1,460,017 cycles.Originality/valueThe originality of this study is that it provides new design ideas for the lightweight design of aircraft load-bearing structures.

Modular design and structural optimization of CubeSat separation mechanism

In order to achieve the design requirements of light weight and high stiffness of the CubeSat modular separation mechanism, a BPNN-GA-PSO size optimization method is proposed to optimize the design of the separation mechanism. Firstly, the separation mechanism of modularization and standardization is analyzed to select the optimization objects. Then, a hierarchical optimization strategy of topology optimization followed by size optimization is adopted to find the global optimum using a hybrid GA-PSO optimization algorithm. Meanwhile, the BPNN surrogate model is introduced to improve the optimization efficiency. The results show that the mass proportion of the optimized separation mechanism is reduced to 18 %, and the maximum deformation of the separation mechanism is 0.123 mm, which meets the design requirements of light weight and high stiffness of the separation mechanism. It proves the applicability of the optimization method to the optimal design of the separation mechanism. The CubeSat modular separation mechanism designed in this paper has been verified by ground verification with overload, vibration, and shock mechanical tests, and successfully deployed the BY-03 satellite in-orbit, which can provide reference for the design and development of subsequent CubeSat modular separation mechanisms.

Enhancing the Transferability of Adversarial Patch via Alternating Minimization

Adversarial patches, a type of adversarial example, pose serious security threats to deep neural networks (DNNs) by inducing erroneous outputs. Existing gradient stabilization methods aim to stabilize the optimization direction of adversarial examples through accumulating gradient momentum to enhance attack transferability on black-box models. However, they are not fully effective for adversarial patches. The accumulated momentum during optimization often misaligns with the optimization direction, reducing their efficacy in black-box scenarios. We introduce an optimization method called Alternating Minimization for Adversarial Patch (AMAP). This method decomposes the original AP into multiple sub-patches and utilizes their update direction to stabilize the optimization of the original AP. Additionally, we propose an adaptive step size optimization method that accelerates convergence and boosts the attack performance. In face recognition tasks, AMAP outperforms baseline methods by a remarkable 5.21% and exceeds the second-best method by 1.4%. Furthermore, AMAP demonstrates practical feasibility in the physical domain, highlighting its potential for robust computer security testing applications.

Optimal sizing and cost analysis of hybrid energy storage system for EVs using metaheuristic PSO and firefly algorithms

This paper presents a single-objective function optimization method for the optimal sizing and cost of a hybrid energy storage system (HESS) that integrates lithium-ion batteries (LIB) and supercapacitors (SC) for electric vehicle (EV) applications. The study introduces a comprehensive framework for EV modeling, incorporating simulated EV data to enhance accuracy. A key achievement involves adapting the modified–WLTC driving cycle, iteratively employed in EV simulations to accurately capture the spectrum of power and energy profiles within the designated range, ensuring adherence to BMW-i3's top speed requisites. The proposed method's validity is established by comparing optimization results using Particle Swarm Optimization (PSO) and Firefly Algorithm (FA), indicating comparable HESS sizing and cost outcomes. Notably, the PSO algorithm demonstrates superior accuracy and computational efficiency. Through PSO, the optimal LIB-SC HESS weight is determined at 160 kg with an optimal cost of $27,660, while FA yields 161 kg and $28,270, surpassing LIB-Only EV models by approximately 21 % in improved sizing. This research significantly contributes by establishing a robust EV modeling paradigm, employing simulated data for optimization, and successfully implementing PSO to determine optimal HESS parameters, advancing the field of efficient EV energy storage and promoting cost-effective, sustainable electric transportation.

Joint Sizing Optimization Method of PVs, Hybrid Energy Storage Systems, and Power Flow Controllers for Flexible Traction Substations in Electric Railways

Joint Sizing Optimization Method of PVs, Hybrid Energy Storage Systems, and Power Flow Controllers for Flexible Traction Substations in Electric Railways

A T-splines-oriented isogeometric topology optimization for plate and shell structures with arbitrary geometries using Bézier extraction

Recently, the non-uniform rational B-splines (NURBS) have been considerably employed in modeling plate and shell structures, or developing the related size, shape or topology optimization methods for their design. However, the NURBS with the tensor product feature strongly hinders the effectiveness of the optimization on complex structures. The primary intention of the current research is to propose a new T-splines-oriented Isogeometric Topology Optimization (T-ITO) method and then address the critical design problem of plate and shell structures with arbitrary shapes in practical applications. Firstly, the Bézier extraction is utilized in the T-splines to map the geometrical model into a family of Bézier elements, each of which is presented by a local Density Distribution Function (DDF) for elementary topology. A global DDF is constructed by an assembly of all local DDFs with a natural connection to the present structural topology. Secondly, the T-splines-based IsoGeometric Analysis (T-IGA) formulation with the Bézier extraction for complex plate and shell structures is developed using the Kirchhoff-Love theory, where a universal numerical implementation framework is constructed, including the Rhino, MATLAB, export modules with all geometric information and import modules for the analysis. Thirdly, a mathematical formulation for plate and shell structures with arbitrary geometries is developed using the T-ITO method to improve structural loading-capability, and the sensitivity analysis with respect to design variables is rigorously derived in detail. Finally, several numerical examples of plate and shell structures with both regular and elaborate geometries are tested to demonstrate the validity, effectiveness, superiorities and indispensability of the T-ITO method.

Maximizing efficiency in sunflower breeding through historical data optimization

Genomic selection (GS) has become an increasingly popular tool in plant breeding programs, propelled by declining genotyping costs, an increase in computational power, and rediscovery of the best linear unbiased prediction methodology over the past two decades. This development has led to an accumulation of extensive historical datasets with genotypic and phenotypic information, triggering the question of how to best utilize these datasets. Here, we investigate whether all available data or a subset should be used to calibrate GS models for across-year predictions in a 7-year dataset of a commercial hybrid sunflower breeding program. We employed a multi-objective optimization approach to determine the ideal years to include in the training set (TRS). Next, for a given combination of TRS years, we further optimized the TRS size and its genetic composition. We developed the Min_GRM size optimization method which consistently found the optimal TRS size, reducing dimensionality by 20% with an approximately 1% loss in predictive ability. Additionally, the Tails_GEGVs algorithm displayed potential, outperforming the use of all data by using just 60% of it for grain yield, a high-complexity, low-heritability trait. Moreover, maximizing the genetic diversity of the TRS resulted in a consistent predictive ability across the entire range of genotypic values in the test set. Interestingly, the Tails_GEGVs algorithm, due to its ability to leverage heterogeneity, enhanced predictive performance for key hybrids with extreme genotypic values. Our study provides new insights into the optimal utilization of historical data in plant breeding programs, resulting in improved GS model predictive ability.

Research on Vehicle Frame Optimization Methods Based on the Combination of Size Optimization and Topology Optimization

The efficient development of electric vehicles is essential to drive society towards sustainable development. Designing a lightweight frame is a key strategy to improve the economy and environment, increase energy efficiency, and reduce carbon emissions. Taking an automatic loading and unloading mixer truck as the research object, a force analysis of its frame was conducted under six typical working conditions. A size optimization method based on a hybrid model of the Kriging model and the analytic hierarchy process (AHP) is proposed. An approximate model of the mass and maximum stress of the frame was established using the Kriging model, and the Kriging model was optimized by using the multi-objective genetic optimization algorithm and the AHP method. Meanwhile, topology optimization was introduced to improve the structural performance of the frame and reduce its weight. The optimization results show that the overall weight of the frame is reduced by 11.96% compared to the pre-optimization period, though it still meets the material performance specifications. By comparing the iterative curves of the single Kriging model with those of the AHP model, it can be seen that the initial optimization efficiency of the hybrid model is about twice as much as that of the AHP model, and the final optimization result is improved by about 3.6% compared with the Kriging model. This validates the hybrid model as an effective tool for the multi-objective optimization of electric vehicle frames, providing more efficient and accurate optimization results for frame design.

An efficient k-NN-based rao optimization method for optimal discrete sizing of truss structures

This study proposes a new method called k-nearest neighbor comparison (k-NNC) to address the computational cost issue of truss optimization with discrete variables using metaheuristic algorithms. The k-NNC judges a new design candidate is worth evaluating by comparing its k available closest designs (k-nearest neighbors) with another design in the population. The new design will be eliminated without evaluating it if the majority of the k nearest neighboring designs are inferior to the one being compared. The k-NNC is combined with Rao algorithms along with Deb’s constraint handling rules and the rounding technique to be suitable for constrained optimization problems with discrete variables. The new optimization Rao algorithms based on k-NNC are used in five truss optimization examples, including both planar trusses and spatial trusses, to evaluate their effectiveness. The numerical results demonstrate that the proposed k-NNC-based Rao algorithms outperform the original Rao algorithms in terms of computational costs. Moreover, the overall performance of k-NNC-based Rao algorithms is similar to or better than that of some state-of-the-art metaheuristic algorithms conducted on the same examples.

An Iterative Heuristic Optimization Method for the optimum Sizing of Battery Energy Storage System to augment the dispatchability of Wind Generators

An Iterative Heuristic Optimization Method for the optimum Sizing of Battery Energy Storage System to augment the dispatchability of Wind Generators

Multi-Objective Sizing Optimization Method of Microgrid Considering Cost and Carbon Emissions

Multi-Objective Sizing Optimization Method of Microgrid Considering Cost and Carbon Emissions

Finite Element Analysis and Optimization of Hydrogen Fuel Cell City Bus Body Frame Structure

Hydrogen fuel cell city bus is a type of new energy public transportation. In this paper, in order to evaluate the safety performance of a newly developed hydrogen fuel cell city bus body frame designed by the collaborating enterprise, finite element analysis is conducted to investigate its structural mechanics and dynamic characteristics under four typical operating conditions, including horizontal bending, ultimate torsion, emergency cornering, and emergency braking. Based on the simulation results, although the body frame of the bus meets the stiffness design requirements and avoids body resonance, it exhibits maximum stresses of 328.9 MPa and 348.6 MPa under emergency cornering and ultimate torsion conditions, respectively, exceeding the material yield strength and failing to satisfy the strength design requirements. Therefore, the size optimization method is employed to optimize the thickness of the body frame components. After optimization, the maximum stresses are reduced to 262.7 MPa and 300.6 MPa, respectively, representing a reduction of up to 20.13%. The optimization significantly improves performance and meets the strength design requirements. Furthermore, the body frame is lightened by 106 kg, achieving the goal of weight reduction.

Discrete sizing optimization method based on dividing rectangles algorithm and local response surface for steel frame structures

It has always been the goal of structural engineers to construct safe and stable buildings using the least amount of materials. Utilizing a quick and effective way to optimize the cross-section size is crucial because conventional building design methods are impacted by the designers' knowledge, and it is challenging to prevent material waste. Due to its implicit optimization function, discrete design variables, and expensive individual evaluation, sizing optimization of high-rise buildings is very challenging to achieve. In order to effectively handle this optimization problem, a two-stage discrete sizing optimization method for high-rise buildings based on the DIviding RECTangles (DIRECT) algorithm and local response surface is proposed in this paper. The optimization method suggested in this research consists of two key stages: the global search stage and the local search stage. In the global search stage, the entire design domain is divided using a modified DIRECT algorithm to swiftly identify potentially optimal subregions that may contain the best points. In the local search stage, the local response surface model is constructed to approximate the objective and constraint functions using the sampling points from the previous stage, and the discrete optimal solution is rapidly found through mathematical iterative solving. A sizing optimization calculation program for high-rise buildings was developed in Microsoft Visual Studio 2015 on the basis of C++ to achieve automatic optimization. The new method was applied to optimize three high-rise steel frame buildings with different heights and plan shapes. The results showed that the material cost could be successfully saved compared with the conventional design, and the over-limit constraints could be adjusted automatically, which demonstrated the viability and efficacy of the two-stage optimization method.

Bi-layer sizing and design optimization method of propulsion system for electric vertical takeoff and landing aircraft

Electric vertical takeoff and landing (eVTOL) aircraft is a promising solution for future urban air mobility, but it suffers from insufficient energy and power density as opposed to fossil fuel-based aircraft. This paper proposes a bi-layer design optimization method for electric propulsion systems. To tackle the issue of high-dimension design parameters and multiple performance constraints, the design process is divided into two parameter optimization problems. In the upper layer, only scalar parameters of components are considered to determine a feasible sizing result subject to energy-flow limits. The outcome of this layer further serves as the initial guess of the lower design layer. The constraints of power conservation and conversion among components are taken into account. The optimization is scheduled in a cascaded paradigm that maximizes the total energy efficiency with minimized total weight. The blade-element momentum method is employed to estimate propeller thrust and torque coefficients. The proposed method is applied to a tilt-wing eVTOL. The sizing results shows that the eVTOL mass is reduced by 11.66% as opposed to the benchmark method. The proposed method provides a generic framework for sizing and design optimization independent of configuration and mission profiles.

Design and Optimization of Multifunctional Human Motion Rehabilitation Training Robot EEGO

A multifunctional human motion rehabilitation training robot named EEGO (electric easy go) that could achieve four functions through structural transformation was designed. The four functions achieved by four working modes: the Supporting Posture Mode (SM), the Grasping Posture Mode (GM), the Riding Posture Mode (RM), and the Pet Mode (PM), which are suitable for patients in the middle and late stages of rehabilitation. The size of the equipment under different functions is determined by the height of different postures of the human. During the design process, the equipment was lightweight using size optimization methods, resulting in a 47.3% reduction in mass compared to the original design. Based on the Zero Moment Point (ZMP) stability principle, the stability mechanism of the robot was verified under the three different functions. According to the wanted function of the equipment, the control system of the equipment was designed. Finally, a prototype was prepared based on the analysis and design results for experimental verification, which can effectively assist patients in motion rehabilitation training such as gait, walking, and other movements.

A Two-Stage Multi-Scenario Optimization Method for Placement and Sizing of Soft Open Points in Distribution Networks

The Soft Open Point (SOP) is an emerging power electronic device for active power flow control, reactive power compensation and post-fault service restoration in power distribution networks. In this paper, a novel two-stage multi-scenario optimization method for SOP placement and sizing is proposed to minimize power losses and active power curtailment of distributed generation (DG) in distribution systems. In Stage 1, the Loss Sensitivity Index (LSI) and Voltage Deviation Index (VDI) are used to identify candidate locations of SOPs, and a new method to estimate the LSI is proposed. In Stage 2, a mathematical optimization model is developed using AC power flow to optimally size each voltage source converter (VSC) of a SOP; the power factor constraint at the substation/grid interconnection is incorporated in this model to ensure security of the upstream grid. The proposed method is validated by a modified IEEE 33-node test system and a large 404-node unbalanced distribution system operated by Saskatoon Light and Power in Saskatoon, Canada.

Simplified local infill size optimization for FDM printed PLA parts

The great advantage of additive manufacturing is the fact that hollowed parts with a given infill can be created. However, the standardized commercial slicer software offers a uniform infill pattern creation solution. In engineering practice, the manufactured parts are functional, therefore the appropriate load bearing capacity is mostly mandatory. In this paper a simplified local infill size optimization method has been presented. Based on a Finite Element Analysis the local density of the pattern can be adjusted, according to the emerged local stresses. The results show that independently of the pattern type, if the scaling was applied, the mechanical resistance was improved to the same extent. In case of the worst-performing uniform pattern, 84% improvement in mechanical resistance was achieved with the optimization. In addition, an FDM printing problem has been highlighted, which must be eliminated if the proposed method is used.

A novel multi-objective topology–size integrated design strategy

In this article, a new multi-objective topology and size optimization framework is developed for the joint concept–detailed design of stiffened panels. The normalized exponential weighted criterion (NEWC) is suggested to explore the Pareto-optimal topology point. A triple-weighting strategy (TWS) using the optimal combined weight model (OCWM) is presented to derive ideal sub-objective weights desired by NEWC. A three-stage continuation (TSC) technique with penalization power increasing by one is introduced to assist NEWC in multi-objective topology optimization considering symmetry constraints. Then, a segmented size optimization (SSO) method assisted by automation technology is used for the curved stiffened panel obtained by NEWC to fulfil the detailed design. Comparisons with the classical dual-weighting methods, average method (AM), and weighted sum method (WSM) verify the validity of TWS, OCWM and NEWC, respectively. The contrast of the multi-objective detailed design with the typical stiffened panel illustrates the improvement of SSO.

Application Research on the Lightweight Design and Optimization of Carbon Fiber Reinforced Polymers (CFRP) Floor for Automobile.

In order to improve the lightweight level of the automotive floor, reduce material application cost, and improve integrated process manufacturing performance through structural design and optimization, this article proposes a design method to link conceptual design and detailed design and optimize the composite floor by combining free size optimization and size optimization methods. The basic theory of composite mechanics is expounded from the stress-strain theory of single-layer plates, and the stiffness and strength theory of laminated plates, which provides theoretical support for the structural design, material design, and allowable value design of composites. The mechanical properties of CFRP were tested to obtain the basic material parameters of CFRP T300/5208. With the material parameters, the CFRP floor super layers are established in Optistruct software. The shape of the floor super layers is optimized by using the free size optimization method, with the body-in-white (BIW) lightweight coefficient as the objective and the BIW performance as the constraints. The BIW lightweight coefficient is reduced from 4.35 to 4.20 after free size optimization, and the layer blocks shape is obtained and clipped based on engineering application. With the floor mass as the objective and the BIW performance as the constraints, the size optimization of the floor layer blocks thickness is optimized. Then the number of floor layers is obtained, and the CFRP floor is established in Fibersim software. Use the simulation analysis method to compare and verify the performance of the floor before and after optimization. The results show that the failure index of the floor is far less than the failure standard, while the mass of the CFRP floor is reduced by 6.8 kg compared with the original steel floor, which an improvement rate reaching 27.5%. The design and optimization methods presented in this article provide a reference for the design and application of the CFRP floor.

Design of a 2 m Primary Mirror Assembly Considering Fatigue Characteristics

The design requirements of a 2 m mirror assembly installed on a large space optical remote sensor are investigated in this study. The mirror body and rigid connectors are designed using topology and size optimization methods. The initial design scheme of flexible supports is then proposed through an in-depth exploration of fatigue failure mechanism caused by impacts of size parameters, thermal–mechanical vibration, and surface abrasion. The comprehensive analysis mode of the flexible support structure is established with the help of finite element and fatigue analysis software programs. The designed mirror surface error under composited force is 5.6 nm, first natural vibration mode is 119.16 Hz, and fatigue life synthesizing thermal–mechanical vibration is about 21,790,000 cycles, thereby meeting the design requirements. The first natural vibration mode, acceleration response, and stress responses of sine and random vibration are verified with experiments, and the findings are compared with the theoretical analysis results. The analysis mode can successfully and significantly improve the reliability of the mirror assembly and help optimize the design of flexible supports.