Intelligent Optimization Design Research Articles

Investigation and Analysis of the Contribution of Chinese Electric Vehicle Social Organizations’ Standardization Innovation to Intelligent Optimization Research and Development Investment

Intelligent design has been the direction pursued by international electric vehicle (EV) research and development (R&D) teams in recent years. This paper analyzes the problems of unsustainable development in the current product design of EVs in China, such as high R&D investment, high innovation risks, and low R&D input–output ratios. It explores the issues related to intelligent design, R&D investment, car prices, and safety in the field of EVs in China, and it proposes the concept of optimizing intelligence to optimize the design investment of EVs in China. On the basis of the development situation and the existing problems of social organization standards that gather innovative technologies for EVs, this paper used data from the national social organization standard information platform as the research object and analyzed important data, such as the quantity of the information of relevant social organizations and professional fields of social organization standards, through mathematical methods. The article proposes an optimization design scheme for EV products in China, combining intelligence and practicality from the perspective of the optimizing intelligent design, and it models the construction of EV optimization design. The quantitative relationship between the two schemes before and after optimization design is compared in terms of cost savings in intelligent design, the improvement of social benefits, and the enhancement of EV cost performance. The comparative study found that intelligent optimization design reduced the R&D cost of EVs by 45.24%, and the social benefits of R&D investment increased by 29.51%.

Multi-Objective Intelligent Optimization Design Method of Microstrip Antenna Based on Back Propagation Neural Network

Multi-Objective Intelligent Optimization Design Method of Microstrip Antenna Based on Back Propagation Neural Network

The multi-stage adding strategy on Kriging and applied to cavitation optimization of centrifugal impeller

In order to realize the intelligent optimization design of cavitation performance of fuel centrifugal pump, a multi-stage active learning adding strategy based on Kriging is proposed. The focus is to establish the three-stage adding strategy by using MSE, EI and CV, and clarify the switching criteria for each stage. Based on the test functions such as one-dimensional function and multi-dimensional function, it is compared with the single-stage adding strategy to verify the effectiveness of the proposed strategy. Furthermore, for a certain type of fuel centrifugal pump, the optimization application of the proposed strategy in its cavitation characteristics is completed. The result of the test function calculation shows that the proposed strategy takes slightly longer to achieve the same accuracy than MSE, which is better than EI and CV. However, compared with MSE, the local accuracy of the global optimal solution is higher. The result of centrifugal pump case shows that the pressure loss at the inlet of the optimized pump decreases, the local flow such as return flow and vortex at the inlet is weakened, the pressure gradient and the vortex flow in the volute weakens. Moreover, the cavitation coefficient of the optimized pump is increased by 30.83%, the NPSH allowance is reduced by 9.14%, the cavitation ratio is reduced by 7.17%, and the gas content in the pump is reduced by 17.27%. Thus, the proposed method improves the cavitation performance of the centrifugal pump effectively.

Optimization of Hydraulic Efficiency and Internal Flow Characteristics of a Multi-Stage Pump Using RBF Neural Network

In order to improve the hydraulic efficiency and internal flow pattern of a multi-stage pump under multiple flow conditions, an intelligent optimization design was proposed for its hydraulic components. Sensitivity analysis was used to select the key parameters influencing the hydraulic efficiency of a multi-stage pump. The optimal Latin hypercube sampling and non-dominated sorting genetic algorithm Ⅱ were employed to build a multi-objective optimization system. Moreover, a radial basis function neural network was adopted as the surrogate model of hydraulic efficiency. The research results showed that the impeller outlet width, impeller blade wrap angle, impeller outlet blade angle, and diffuser inlet width were the key factors affecting the hydraulic efficiency. The efficiency of the optimized model increased by 4.35% under the design condition and the matching of the internal flow between the optimized impeller and diffuser was significantly enhanced under the nominal condition. The improved flow pattern could be clearly observed in the flow passage of both the pump impeller and the diffuser. After optimization, the wear performance of the model was also improved compared to the original design. The wear area decreased in size and was distributed more evenly, resulting in a noticeable decrease in the maximum amount of wear.

Models, simulations, and applications of small satellite thermal analysis

Progress in satellite technology places higher expectations on the design period and reliability of thermal control systems, especially for small satellites with high intensification. Here, a thermal analysis is conducted throughout the satellite development process, with thermal modeling serving as the foundation for thermal analysis in the early and formal design and test verification stages. As a result, designers are required to appreciate, penetrate, and properly apply thermal models for the development of satellite thermal systems. Thus, in this article, we reviewed the research and development of thermal model techniques, as well as the preparation of crucial parameters and means of simplification for accurate and fast solutions. In addition, a summary of the relevant application and state-of-the-art technologies such as uncertainty and sensitivity analysis, intelligent optimization design, and parameter correlation, was dedicated to promoting the comprehensive development and innovation of thermal analysis of satellites.

Intelligent optimization design of squeeze casting process parameters based on neural network and improved sparrow search algorithm

Squeeze casting process parameters are the key to squeeze casting production and to obtain excellent performance casts. To realize intelligent optimization design of process parameters under various requirements, this work presents a new intelligent optimization design framework for squeeze casting process parameters based on process data and integrating a two-stage intelligent integrated optimization. To adapt to diverse optimization applications of different process and target parameters, the backpropagation (BP) neural network and existing process data are utilized to intelligently establish the incompletely determined correlations between process parameters and squeeze cast quality or properties. An improved sparrow search algorithm (LCSSA) is developed and integrated to optimize both the aforementioned model structure and intelligently obtain the optimal solution, that is, the two stages of optimization. For the purpose of assessing the effect of each performance on the combination of process parameters, the information entropy weight approach is used. Various application cases and experiments have been conducted to evaluate the effectiveness of the suggested intelligent optimization design framework. It is indicated that the proposed method is practicable and can intelligently achieve ideal process parameters with high accuracy even based on small-scale data samples. The solving efficiency and optimization accuracy of LCSSA are superior than those of other intelligent optimization algorithms like the genetic algorithm (GA). The proposed framework outperforms the mixture of the BP neural network and other traditional optimization algorithms.

Intelligent optimization design of large-scale three-dimensional ultrasonic vibration system

Large-scale three-dimensional ultrasonic vibration systems are susceptible to the influence of coupled vibration, resulting in a series of problems such as increased energy loss, small longitudinal displacement amplitude of the radiation surface, and uneven distribution of longitudinal displacement amplitude, which can seriously affect the working efficiency of ultrasonic processing system. How to effectively control the coupled vibration of large-scale three-dimensional ultrasonic transducer systems and optimize their performance has become an urgent problem in the field of power ultrasound. The research has found that some phononic crystal slots and point defect structures can suppress the lateral vibration of large-scale transducer systems, improve the uniformity of system amplitude distribution, and artificially regulate the performance of large-scale three-dimensional ultrasonic vibration systems by changing the configuration parameters of phononic crystal structures. However, excessive design parameters will inevitably increase the complexity of system design, and, currently, the optimization design of large-scale three-dimensional ultrasonic transducer systems relies on empirical trial and error methods which has low design efficiency and low success rate, and cannot guarantee the system performance. Therefore, in the study, homogeneous dislocations and point defect structures are introduced to optimize the design of large-scale three-dimensional ultrasonic vibration systems. Data analysis techniques are used to evaluate the influences of the configuration of homogeneous dislocations and point defect structures on the longitudinal displacement amplitude, amplitude distribution uniformity, radiated sound power, working bandwidth of the system’s radiation surface. And a predictive model for the performance of large-scale ultrasonic transducer system with homogeneous dislocation structure and near periodic defect structure is established, which can achieve intelligent design of large-scale power ultrasonic transducer system, improve design efficiency and success rate, and reduce the design cost.

Intelligent optimization design method for horizontal well trajectory in Longdong shale oil

Intelligent optimization design method for horizontal well trajectory in Longdong shale oil

Intelligent Optimization Design of a Phononic Crystal Air-Coupled Ultrasound Transducer.

To further improve the operational performance of a phononic crystal air-coupled ultrasonic transducer while reducing the number of simulations, an intelligent optimization design strategy is proposed by combining finite element simulation analysis and artificial intelligence (AI) methods. In the proposed strategy, the structural design parameters of 1-3 piezoelectric composites and acoustic impedance gradient matching layer are sampled using the optimal Latin hypercube sampling (OLHS) method. Moreover, the COMSOL software is utilized to calculate the performance parameters of the transducer. Based on the simulation data, a radial basis function neural network (RBFNN) model is trained to establish the relationship between the design parameters and the performance parameters. The accuracy of the approximation model is verified through linear regression plots and statistical methods. Finally, the NSGA-II algorithm is used to determine the design parameters of the transducer. After optimization, the band gap widths of the piezoelectric composites and acoustic impedance gradient matching layer are increased by 16 kHz and 13.5 kHz, respectively. Additionally, the -6 dB bandwidth of the transducer is expanded by 11.5%. The simulation results and experimental results are consistent with the design objectives, which confirms the effectiveness of the design strategy. This work provides a feasible strategy for the design of high-performance air-coupled ultrasonic transducers, which is of great significance for the development of non-destructive testing technology.

Intelligent Optimization Design of Distillation Columns Using Surrogate Models Based on GA-BP

The design of distillation columns significantly impacts the economy, energy consumption, and environment of chemical processes. However, optimizing the design of distillation columns is a very challenging problem. In order to develop an intelligent technique to obtain the best design solution, improve design efficiency, and minimize reliance on experience in the design process, a design methodology based on the GA-BP model is proposed in this paper. Firstly, a distillation column surrogate model is established using the back propagation neural network technique based on the training data from the rigorous simulation, which covers all possible changes in feed conditions, operating conditions, and design parameters. The essence of this step is to turn the distillation design process from model-driven to data-driven. Secondly, the model takes the minimum TAC as the objective function and performs the optimization search using a Genetic Algorithm to obtain the design solution with the minimum TAC, in which a life-cycle assessment (LCA) model is incorporated to evaluate the obtained optimized design solution from both economic and environmental aspects. Finally, the feasibility of the proposed method is verified with a propylene distillation column as an example. The results show that the method has advantages in convergence speed without sacrificing accuracy and can obtain an improved design solution with reduced cost and environmental impact. Compared with the original design using rigorous simulation, the TAC is reduced by 6.1% and carbon emission by 27.13 kgCO2/t.

Intelligent multiobjective optimization design for NZEBs in China: Four climatic regions

Near-zero-energy-consumption buildings (NZEBs) are of great significance for sustainable development, and their design and research have attracted increasing academic attention. To drive and realize the energy saving design and achieve carbon emission and thermal comfort optimization of NZEBs, in this paper, an intelligent optimization method integrating the BIM-DB and PSO-RF-NSGA-III method is established. Multiobjective optimization problems involving NZEBs in four typical climate regions in China are explored. With a typical office building as an example, first, simulation calculations regarding the energy consumption, carbon emissions and indoor thermal comfort in the four climatic regions are performed based on orthogonal tests and BIM-DB. Second, the nonlinear mapping relationships between building design parameters and prediction targets are constructed with the PSO-RF model, which is trained with sample data. The obtained nonlinear mapping relations are used to establish the objective function of NSGA-III, and the multiobjective Pareto-optimal solution set is obtained with the developed PSO-RF-NSGA-III algorithm. Finally, the only optimal solution is determined by using the ideal point method, and reference near-zero-energy-consumption office building parameters are calculated for different climate regions. The conclusions are as follows. (1) The PSO-RF algorithm can efficiently predict building energy consumption, carbon emissions and thermal comfort. In the four regions, the goodness of fit of the three targets is greater than 0.94. (2) Multiobjective optimization can be performed with the proposed RF-NSGA-III intelligent optimization method. After optimizing multiple groups of optimization schemes and adopting energy saving measures, the energy consumption levels in the four climate regions are reduced by 39.72 %, 32.22 %, 26.94 % and 35.37 %, and the other goals are optimized. (3) Index calculations indicate that the optimized building design parameters meet the specified standards for NZEBs, and the main influencing factors and corresponding measures vary from region to region.

Optimization of Savonius wind turbine blades based on perturbed stochastic fractal search algorithm and Kriging surrogate model

ABSTRACT The shape of its blades would significantly affect the energy capture ability of the Savonius wind turbine. This work proposed an efficient and intelligent optimization design method for the shape of the blade. Firstly, the blade’s shape is described parametrically using the cubic Bezier curve and then a certain number of design schemes in the design space are obtained using the Latin hypercube sampling technique, and the torque coefficients of these design schemes are evaluated using CFD simulation. Secondly, a Kriging model based on these design schemes and their corresponding torque coefficients is established. Thirdly, the optimal design scheme is obtained by taking the established Kriging model as an evaluation tool and taking the perturbed stochastic fractal search algorithm as an optimization tool and then verified using CFD. Through the above optimization method, a new type of blade is obtained. Compared with the traditional turbine with semi-circular blades, the turbine with the optimized blades can achieve about 9.03% improvement in energy capture efficiency at TSR = 1.

Multi-Objective Optimization Design Method for Whole-Aeroengine Coupling Vibration

With the increasing thrust-to-weight ratio of modern aeroengines and the widespread adoption of thin-walled casing, the impact of the coupling vibration effect between the rotor and stator on the critical speed, rotor–stator mode shape coordination, and response characteristics of the whole engine have become increasingly prominent. However, the vibrational design of the whole-engine coupling has thus far relied on human experience, revealing a need for the study of its intelligent optimization. In this study, to quantitatively analyze the vibration of the whole engine, three vibration evaluation indexes were defined, which were risk coefficients for the critical speed, rotor strain energy, and cross-section rotor–stator rubbing. Using these indexes as the optimization targets, a multi-objective intelligent optimization design method for aeroengine support stiffness was proposed. The design optimization of the support stiffness of a turbofan engine with a large bypass ratio was performed. The results showed that the vibration index of the whole machine can be reduced to different degrees and that the whole-engine coupling vibration is optimized.

Retracted: Intelligent Optimization Design of Automatic Sorting Robot Process

Retracted: Intelligent Optimization Design of Automatic Sorting Robot Process

Intelligent optimization design of electron barrier layer for AlGaN-based deep-ultraviolet light-emitting diodes

AlGaN-based deep-ultraviolet light-emitting diodes (DUV LEDs) are widely used in sterilization, sensing, water purification, medical treatment, non-line of sight (NLOS) communication and many other fields. Especially it has been reported that the global novel coronavirus (COVID-19) can be effectively inactivated by the DUV light with a wavelength below 280 nm (UVC) within a few seconds, which has also attracted great attention. However, the external quantum efficiency (EQE) of UVC LED is still at a low level, generally not more than 10%. As an important component of EQE, internal quantum efficiency (IQE) plays a crucial role in realizing high-performance DUV-LED. In order to improve the IQE of AlGaN-based DUV-LED, this work adopts an electron blocking layer (EBL) structure based on InAlGaN/AlGaN superlattice. The results show that the superlattice EBL structure can effectively improve the IQE compared with the traditional single-layer and double-layer EBL structure for the DUV-LED. On this basis, the optimization method based on JAYA intelligent algorithm for LED structure design is proposed in this work. Using the proposed design method, the InAlGaN/AlGaN superlattice EBL structure is further optimized to maximize the LED’s IQE. It is demonstrated that the optimized superlattice EBL structure is beneficial to not only the suppression of electron leakage but also the improvement of hole injection, leading to the increase of carrier recombination in the active region. As a result, the IQE of the DUV-LED at 200 mA injection current is 41.2% higher than that of the single-layer EBL structure. In addition, the optimized structure reduces IQE at high current from 25% to 4%. The optimization method based on intelligent algorithm can break through the limitation of the current LED structure design and provide a new method to improve the efficiency of AlGaN-based DUV-LED.

Investigation on small volume and long optical path multipass cell using differential evolution algorithm

AbstractTo solve the contradiction between high performance and portability of tunable diode laser absorption spectroscopy (TDLAS) systems, we propose a multipass cell (MPC) design method based on differential evolution (DE) algorithm, and realize intelligent optimization design of MPC with small volume and long optical path. MPC is the core component of the TDLAS system. The optical path of MPC determines the detection accuracy of the TDLAS system. The optical path is affected by the position and angle of incident laser, mirror spacing, and other parameters. The DE algorithm is used to optimize the parameters that affect the performance of MPC, and a compact MPC with a spherical mirror diameter of 25.4 mm, a laser reflection number of 183, an optical path of 5.98 m, and a mirror spacing of 31.7 mm is designed. Under the same spherical mirror condition, the detection limit of MPC designed based on the DE algorithm is increased from 0.91 to 0.76 ppm compared with the MPC commonly used seven‐ring spot pattern. This has a guiding and exemplary role in the design and development of high space utilization MPC.

Aerodynamic Data-Driven Surrogate-Assisted Teaching-Learning-Based Optimization (TLBO) Framework for Constrained Transonic Airfoil and Wing Shape Designs

The surrogate-assisted optimization (SAO) process can utilize the knowledge contained in the surrogate model to accelerate the aerodynamic optimization process. The use of this knowledge can be regarded as the primary form of intelligent optimization design. However, there are still some difficulties in improving intelligent design levels, such as the insufficient utilization of optimization process data and optimization parameters’ adjustment that depends on the designer’s intervention and experience. To solve the above problems, a novel aerodynamic data-driven surrogate-assisted teaching-learning-based optimization (TLBO) framework is proposed for constrained aerodynamic shape optimization (ASO). The main contribution of the study is that ASO is promoted using historically aerodynamic process data generated during the gradient free optimization process. Meanwhile, nonparametric adjustment of the TLBO algorithm can help relieve manual design experience for actual engineering applications. Based on the structure of the TLBO algorithm, a model optimal prediction method is proposed as the new surrogate-assisted support strategy to accelerate the ASO process. The proposed method is applied to airfoil and wing shape designs to verify the optimization effect and efficiency. A benchmark aerodynamic design optimization is employed for the drag minimization of the RAE2822 airfoil. The optimized results indicate that the proposed method has advantages of high efficiency, strong optimization ability, and nonparametric characteristics for ASO. Moreover, the results of the wing shape optimization verify the advantages of the proposed methods over the surrogate-based optimization and direct optimization frameworks.

Intelligent Optimization Design of Automatic Sorting Robot Process

In order to further improve the efficiency of sorting robots, an algorithm based on artificial intelligence design is evaluated. On the basis of determining the design requirements of belt conveyor and motor selection, the reverse device and return device and camera bracket sorting car are redesigned, and the sorting car is further analyzed by finite element method. All programs are written based on PLC. The results show that the recognition and sorting rate of 100 express surface orders with the same specifications reaches 100% and the sorting rate reaches 94% by analyzing the data recorded in the database, and the average time consumed in each recognition process is 0.0493097 s and 0.047133 s, respectively, in basic and simulated recognition. The difference did not exceed 0.01 s, and the time was basically the same. Therefore, the scheme verifies the rationality of mechanical structure design, visual scheme design, and control system design.

Intelligent Optimal Design of a Membrane Bioreactor Based on Flow Field Quantitative Analysis

Intelligent Optimal Design of a Membrane Bioreactor Based on Flow Field Quantitative Analysis

An intelligent optimization design method of the compressed ratio of backfilling body to avoid backfilling mining-induced environmental damage.

Backfilling mining is a green mining method. The rational design of the compressed ratio of the backfilling body can reduce backfilling mining costs while avoiding mining-induced environmental damage. At present, the enumeration method is a common design method for the compressed ratio of the backfilling body of backfilling mining under buildings (structures). However, the design method has some problems, such as cumbersome processes and time consumption. Therefore, this paper proposes an intelligent optimization design method of the compressed ratio of backfilling body and constructs the design flow of the method. The method takes economic benefits as the optimization goal and takes the mining-induced subsidence of protected objects to meet their fortification requirements as the constraint condition. Within the feasible region of the compressed ratio, the intelligent optimization algorithm is used to search for the minimum compressed ratio that satisfies the constraints. This paper used the method to design the compressed ratio of the backfilling panel in the Dongtan Coal Mine. The research results show that the method can quickly design the optimal compressed ratio compared with the enumeration method and cut the production cost on the premise of the safety of ground buildings (structures).