Multi-island Genetic Algorithm Research Articles

Analysis of factors influencing Y-ring sealing under ultra-high-pressure, high-temperature and low-temperature conditions

Abstract To study the factors affecting the sealing performance of a Y-shaped sealing ring at the stem of a 140-MPa cage throttle valve under -46°25C and 180°C operating conditions, uniaxial tensile tests were conducted on polytetrafluoroethylene (PTFE) and its modified materials. Finite element simulation software was then used to perform a simulation study of the Y-shaped sealing ring. The finite element simulation software was used to perform a sensitivity analysis on the five most critical parameters of the Y-shaped sealing ring and to perform dimensional optimization by using the multi-island genetic algorithm. The results show that compared with 25°C, under a 180°C condition, the contact stress at both ends of the sealing lip decreases when the valve stem moves upward . Under the condition of -46°C, the contact stress at the sealing lip increases when the valve stem goes down, the contact stress at both ends of the sealing lip decreases when the valve stem goes up; and the impact of the stem movement speed on the maximum value of the Y-shaped sealing ring contact stress is small.The design dimensions that have the greatest influence on the maximum von Mises stress and the maximum contact stress of the inner lip of the Y-seal during stem movement are the width and the tip height of the lip, respectively. The optimum values of the design variables are determined and the feasibility of the theoretical design is verified by testing.

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.

Energy management strategy of integrated adaptive fuzzy power system in fuel cell vehicles

Fuel cell vehicles are a reliable solution to address energy shortages. However, when the road conditions are complex, the system distributes power unevenly between fuel cells and lithium batteries, and cannot effectively absorb the energy generated by braking. In response to this issue, an adaptive control strategy is adopted to allocate the required power of the car to two types of batteries in real time. Fuzzy logic is used to continuously optimize the relevant parameters of the controller based on the vehicle state, and a multi-island genetic algorithm is used to optimize the control strategy, enhancing the global search ability of the control strategy and increasing the vehicle’s ability to absorb and reuse the energy generated by braking. The experiment findings denote that the optimized control strategy increases the remaining capacity of lithium batteries by an average of 1.67%, increases energy recovery by an average of 135 W, increases the overall energy recovery rate by an average of 2.8%, and reduces vehicle fuel consumption by an average of 0.24 L/100 Km. It can be concluded that the optimized adaptive fuzzy control strategy can reduce the probability of over-charging and discharging of lithium batteries and improve the battery life. Meanwhile, the optimized strategy can improve the energy reuse rate, reduce vehicle fuel consumption, lower usage costs. The optimized strategy provides a reference for subsequent research on energy management of fuel cell vehicles.

Investigation on reconstruction of internal heat source in biological tissue based on multi-island genetic algorithm

With the rapid development of engineering thermophysics, researches on human biological heat transfer phenomena has gradually shifted from qualitative to quantitative. It is a typical inverse problem of heat conduction that deriving the distribution of internal heat sources from the temperature distribution on the body surface. Differing from traditional numerical methods for solving heat conduction, this paper transforms such an inverse problem of bio-heat transfer into a direct one, thereby avoiding complex boundary conditions and regularization processes. To noninvasively reconstruct the internal heat source and its corresponding 3D temperature field in biological tissue, the multi-island genetic algorithm (MIGA) is used in the simulation module, where the position P(x, y, z) of point heat source in biological tissue and its corresponding temperature T are set as the optimization variables. Under a certain optimized sample, one can obtain the simulated temperature distributing on the surface of the module, then subtract the simulated temperature from the measured temperature of the same surface which was measured using a thermal infrared imager. If the absolute value of the difference is smaller, it indicates that the current sample is closer to the true location and temperature of the heat source. When the values of optimization variables are determined, the corresponding 3D temperature field is also confirmed. The simulation results show the experimental and simulation temperature values of 15.5Ω resistor are 60.75°C and 62.15 °C respectively, with the error of 2.31 %, and those of 30.5Ω resistor are 84.40 °C and 86.33°C respectively, with the error of 2.29 %. The simulated positions are very approximate with those of the real experimental module. The method presented in this paper has enormous potential and promising prospects in clinical research and application, such as tumor hyperthermia, disease thermal diagnosis technology, etc.

An improved multi-island genetic algorithm and its utilization in the optimal design of a micropositioning stage

An improved multi-island genetic algorithm (IMIGA) by integrating various common improvement methods, optimized through D-optimal and analysis of variance (ANOVA) techniques. IMIGA’s performance is validated against various state-of-the-art algorithms across CEC2020 test suite and classic engineering problems, affirming its superiority. IMIGA’s potential in industrial applications is demonstrated by employing it for optimal design in a micropositioning stage, leveraging machine learning (ML) for parameter optimization. With the optimal design parameters, driven by IMIGA and a surrogate ML model, the proposed stage attained a first natural frequency of 55 Hz and maximum strokes of 266.7 μm, 110.9 μm, and 0.7552 mrad in the x-, y-, and θ-directions, respectively. These experimental results closely align with the predicted outcomes from the design phase, highlighting the accuracy and reliability of the employed methodology. Such consistency between experimental measurements and predicted values underscores the efficacy of the design approach in achieving desired performance metrics.

Spatial-temporal characteristics analysis of laser-induced shockwave pressure by reverse optimization with multi-island genetic algorithm

Spatial-temporal characteristics analysis of laser-induced shockwave pressure by reverse optimization with multi-island genetic algorithm

Multi-objective optimization of the aerodynamic performance of butterfly-shaped film cooling holes in rocket thrust chamber

Abstract This study uses Multi-Island Genetic Algorithm (MIGA) and three-dimensional Computational Fluid Dynamics (CFD) software to optimize butterfly-shaped film cooling holes in the upper-stage rocket engine thrust chamber. The goal is to meet thermal protection and thrust requirements at high altitudes without re-ignition. To facilitate an all-encompassing worldwide search, the holes in the optimized design remain at set dimensions. Film continuity and stability at the nozzle outlet are greatly impacted by the hole structure. Inlet and divergence angles have little effect on thrust, according to regression research, but lip height (de) and outlet width (β) have a big impact on cold gas ejection, which affects cooling and thrust. Optimized results lead to a 20.49 K decrease in the monitoring section’s average wall temperature and a 52.8 N boost in thrust by reducing interference between supersonic airflow and extending film stability.

Crashworthiness optimization of variable stiffness B-pillar with thermoplastic composites

This study focuses on enhancing of crashworthiness and lightweight of the automotive B-pillar structure by using continuous carbon fiber reinforced thermoplastic composites (CFRTP) and optimized layer design. Firstly, the B-pillar is divided into four regions with variable stiffness. Quasi-static three-point bending test and numerical simulation are performed to calculate the stiffness of the structure. Then, the four regions of the B-pillar with different CFRTP layers are structurally designed to realize variable stiffness. Considering manufacturing requirements, a draping simulation is employed to predict potential layer defects. The results demonstrate that B-pillar structural performance is enhanced after the reorientation of CFRTP prepreg. Afterward, the crashworthiness of the CFRTP B-pillar considering the ply drop-off is evaluated and compared with the traditional Q235 steel counterparts. The results indicate that the CFRTP B-pillar exhibits superior crashworthiness but a substantial reduction in mass by 53.1%. Finally, the multi-island genetic algorithm (MIGA) is employed to achieve the optimal layer design scheme for lay-up optimization of the CFRTP B-pillar, and the crashworthiness of the CFRTP B-pillar with variable stiffness is further improved. The current work highlights a structural design strategy for CFRTP components for vehicles, which is crucial for the updating of automobile engineering.

Redefinition of failure envelope of composite materials considering uncertainty of test data

After conducting three World-Wide Failure Exercises (WWFEs), it has become apparent that existing strength theories or criteria have certain deficiencies, and no single theory can accurately match all experimental results. As a result, there is a growing focus on the uncertainties present in the macrocosm test data of composite materials. The paper introduces the concept of uncertainty into the collection of test data for the classical Tsai-Wu failure criterion and Hashin criterion. The method proposed involves fitting the coefficients of the failure criteria by treating experimental data as random variables, a technique that diverges significantly from the conventional method of using composite allowable values to define these coefficients. It also uses the probability of success as a constraint and defines the fitted error as an optimization objective. Monte Carlo sampling is employed to perform a reliability analysis, and six sigma is used for a reliability-based optimization of the failure criteria. The Multi-Island Genetic Algorithm is utilized to search for the optimal value. The approach presented in this study provides the ability to predict future failures in composite materials by considering the uncertainty of the test data. By incorporating uncertainties into the analysis and design process, a more accurate and reliable assessment of the performance of composite materials can be achieved. Moreover, there seems to be a significant difference in the fitting error between the mean data and the normal distribution data. Additionally, when Hashin criterion is modified, the test data matches the failure envelope more closely and the degree of fit between the experimental data and the failure envelope is higher.

Research on Noise Reduction of Water Hydraulic Throttle Valve Based on RBF Neural Network and Multi-Island Genetic Algorithm

Excessive pressure drop within the internal flow channel of the water hydraulic throttle valve will generate severe noise. In order to reduce the noise of the throttle valve, in this paper, the model of the throttle valve was established, and the flow characteristics and acoustic characteristics of the valve were simulated. The simulation results showed that the parameters of the throat structure, such as the half angle, throat inlet angle and throat length, have a significant effect on the noise of the valve. Then, the three main structural parameters were used as optimization variables, and radial basis function (RBF) neural networks and multi-island genetic algorithms (MIGA) were used to reduce the noise of the valve. The approximate model of the relationship between the structural parameters of the valve and noise was established by RBF neural networks, and MIGA was used to optimize the approximate model. Finally, the optimal valve model was established based on the obtained optimal parameters, and its noise was analyzed through simulation and experiment. The research results indicated that the optimization method, which combines RBF Neural Network and MIGA, can effectively reduce the noise of hydraulic throttle valves.

Effect of parameter optimization on the flow characteristics of venturi-self-excited oscillation mixer based on response surface model and multi-island genetic algorithm

The present study focuses on the development of a novel venturi-based self-excited oscillation mixer that effectively utilizes the venturi effect to facilitate efficient abrasive intake while simultaneously ensuring effective prevention of backflow through the utilization of the systolic section within the venturi tube. It not only ensures uniform mixing of water and abrasive but also transforms the continuous jet into a pulsed one, thereby significantly enhancing exit velocity. The orthogonal experimental design method and single factor experiment method were employed to investigate the effects of inlet water pressure, water nozzle diameter, abrasive inlet angle, aspect ratio of the self-excited oscillation mixer, and abrasive pipe inlet diameter on the inlet pressure of the abrasive pipe and the velocity of the jet exit in the new mixing device. Approximate response surface models for these parameters were constructed using lsight optimization software, combining the results of orthogonal experimental simulation. By employing a multi-island genetic algorithm, we have globally optimized this innovative mixing device to determine its optimal performance parameters. Subsequently, comparative experiments were conducted to validate the performance of different mixing devices in descaling applications. Through experimental verification, it was found that the venturi-self-excited oscillation mixer exhibits excellent rust removal capabilities in steel plate tests compared to traditional self-excited oscillation mixers. These findings provide valuable guidance for the subsequent design and enhancement of abrasive water jet mixers.

Parametric optimization of rearview mirror structure based on a multi-island genetic algorithm

To quickly find the structural parameters of a particular mirror that produces the least amount of aerodynamic noise, the rearview mirror of a passenger car is taken as the research object and an optimization platform is established based on Isight software in this paper. The mirror structure is parameterized in Sculptor, and several sample models are constructed using the optimal Latin hypercube design (OLHD). Then, by simulating each sample model, the sound pressure level of the Curle source at the side window surface monitoring point was obtained. The relationship between the mirror structure and the wind noise at the point is fitted using the Kriging approximation model after the calculation is completed. Finally, the values at each point decreased when the changes at positions 1, 2, 3, and 4 were 2.5 mm, 3.91 mm, 4.5 mm, and 3.6 mm, and the sound pressure level at monitoring point 4 decreased the most, with a reduction of 3.76 dB in wind noise compared with the original model.

Numerical prediction and optimization of aerodynamic noise of straw crushers by considering the straw-crushing process

Straw crops are struck and broken into soft filaments by the high-speed rotating hammers of straw crushers, which disturb the internal airflow field and generate much noise during the operation of straw crushers. To accurately estimate and reduce the aerodynamic noise of straw crushers at the design stage, in this study, first, the coupling method of the discrete element method, bonded-particle model, and computational fluid dynamics were used to obtain the acoustics source data. Next, the Ffowcs Williams–Hawkins theory and the indirect boundary element method were used to predict the aerodynamic noise generated during the straw crushing process. The multi-island genetic algorithm was used to optimize the aerodynamic noise of straw crushers. The results indicate that the simulated and measured total sound pressure levels (TSPLs) at the outlet and inlet differed by 1.43 and 2.12 dB(A), respectively. Additionally, aerodynamic noise at the inlet appears to be primarily influenced by the sound pressure level at the excitation fundamental frequency, while noise at the outlet is primarily influenced by the sound pressure level at the double frequency. Higher sound pressure levels were mainly concentrated at the fundamental frequency and its lower harmonic frequencies, and the sound pressure level gradually decreased with the increase in the frequency. After optimization, the aerodynamic noise TSPL at the inlet decreased from 100.87 to 88.58 dB(A) and at the outlet decreased from 102.26 to 89.62 dB(A). This study provides a methodological reference for aerodynamic noise prediction and the design of low-noise straw crushers.

Inverse parameter identification framework for cohesive zone models based on multi-island genetic algorithm

Composite interfaces are commonly simulated by cohesive zone models with the key challenge being the calibration of interfacial parameters. A novel inverse identification framework is presented in this paper to determine the interface parameters of cohesive zone models. This approach employs the multi-island genetic algorithm to obtain the key parameters of cohesive zone model which can reproduce the experimental observations. Utilizing two independent metrics, namely the load-displacement response and the debonding length history, an objective function is formulated to give the framework inherent robustness. The interface debonding length is used to uniquely determine the debonding properties of the specimen. To demonstrate the feasibility of the proposed framework, the strength-based cohesive zone model is taken as an example. The inverse algorithm is adopted to identify the interface parameters of both the double cantilever beam (DCB) experiments and the fixed-ratio mixed-mode (FRMM) tests. The robustness, accuracy and sensitivity of the framework are validated through the double cantilever beam test. The findings indicate that the numerical results align closely with the experimental data, confirming that the interface parameters identified by the proposed framework can reproduce the experimental results.

RBF-Based Integrated Optimization Method of Structural and Turning Parameters for Low-Floor Axle Bridge

The axle bridge plays a crucial role in the bogie of low-floor light rail vehicles, impacting operational efficiency and fuel economy. To minimize the total cost of the structure and turning of axle bridges, an optimization model of structural and turning parameters was built, with the fatigue life, maximum stress, maximum deformation, and maximum main cutting force as constraints. Through orthogonal experiments and multivariate variance analysis, the key design variables which have a significant impact on optimization objectives and constraints (performance responses) were identified. Then the optimal Latin hypercube design and finite element simulation was used to build a Radial Basis Function (RBF) model to approximate the implicit relationship between design variables and performance responses. Finally, a multi-island genetic algorithm was applied to solve the integrated optimization model, resulting in an 8.457% and 1.1% reduction in total cost compared with the original parameters and parameters of sequential optimization, proving the effectiveness of the proposed method.

Optimization design on cooling structure of High-Temperature magnetic fluid dynamic seal device

In order to better sealing performance of magnetic fluid at high temperature, structural optimization design of cooling component in magnetic fluid dynamic sealing device at high temperature is studied in this paper. The magnetic field model of magnetic fluid sealing device at high-temperature is established to analyze the influence of temperature on the sealing performance of magnetic fluid, and magnetic field strength distribution is obtained. The simulated results show both magnetic induction strength difference and theoretical sealing pressure decrease gradually with the increase of the operating temperature. To reduce the temperature having great influence on sealing performance of magnetic fluid dynamic seal device, the temperature field model is constructed. Based on the Latin hypercube sampling method, the design matrix between cooling structural parameters and temperature is determined. In consideration of the neural network approach, the approximate agent model is built to analyze the correlation relationship between cooling structure parameters and cooling performance. The multi-island genetic algorithm is conducted on searching the optimal cooling structural parameters under global design space. The optimized results show that the average temperature of the permanent magnet is decreased by 27 °C, and the average temperature of the seal gap is reduced by 28 °C. The theoretical sealing differential pressure of the high-temperature magnetic fluid is improved by 49 KPa.

Research on Mode Switching and Energy Management of Hybrid Electric Vehicle

Aiming at the optimization of mode switching control and energy management strategy for parallel hybrid electric vehicles, a mode switching control method and A-ECMS steady-state energy management strategy is proposed. Firstly, in order to effectively suppress the impact caused by mode switching, and a fuzzy controller is designed for clutch engagement displacement. Then, on the basis of ensuring the smoothness of mode switching, A-ECMS control strategy based on SOC feedback is established. Finally, Isight and Cruise are used to build a co-simulation platform, and multi-island genetic algorithm is used to study the influence of initial value selection of equivalent factor on the controller. The results show that the proposed mode switching control and energy management control strategy, combined with A-ECMS strategy, can effectively improve the vehicle fuel economy on the basis of ensuring the mode switching smoothness.

Optimizing and designing upper-stage separation spring of lifting-body rocket

Because the upper-stage separation of a lifting-body rocket is difficult under the low-altitude and high-speed flight environments, an upper-stage separation scheme based on the separation spring is proposed. The integration of ADAMS and MATLAB realizes the multi-body dynamics simulation analysis of upper-stage separation. The experimental design method is used to carry out the target simulation and calculation of the upper-stage separation process, with the separation condition deviation and the separation spring deviation considered at the same time, thus ensuring the reliability of the separation scheme. In order to further optimize the separation scheme, an upper-stage separation optimization model is established with the Isight software, and the mass of separation spring is optimized with the multi-island genetic algorithm and significantly reduced by 48.87% under the condition that the upper-stage separation process satisfies all constraints, thereby effectively improving the performance of the lifting-body rocket.

Cavitation performance analysis and optimal design of the portable electric-driven axial flow pump

Based on the ISIGHT optimization platform, the impeller parameterization modeling and simulation process of the portable axial flow pump is carried out through CFturbo, ICEM and CFX software, and the impeller is optimized with the help of multi-island genetic algorithm to target the two optimization objectives of efficiency and head. The optimization results show that the hydraulic efficiency increased by 6.3% under design conditions. At the same time, compared with the original solution, the optimized cavitation performance is significantly improved. Additionally, overall entropy production is significantly reduced. Field test results show that the head deviation is 1.42% and the efficiency deviation is 4.52% under design conditions, proving that the optimization results are reliable.

Parameters collaborative optimization design and innovation verification approach for fuel cell distributed drive electric tractor

Fuel cell distributed drive electric tractors (FCDET) are one of the necessary means to achieve truly green agriculture. However, low traction efficiency, poor control coordination, and excessive energy consumption are the main reasons hindering the industrialization of FCDET. This paper proposed a parametric collaborative optimization design method and innovative verification system that considers the drive system and energy system. Based on the tractor plowing operating conditions, a 7-DOF coupled dynamics model of the distributed drive system and a tire-soil interaction model were established, and the key component selection and parameter matching were completed. On the drive system optimization level, the front and rear wheelside transmission ratios are optimized based on a multi-island genetic algorithm (MIGA). On the energy system optimization level, the globally optimal power distribution ratio is found based on the dynamic programming algorithm (DP). A complete experimental prototype verification system is constructed based on the MATLAB/Simulink-NI PXI joint simulation platform, physical prototype test bench, and real vehicle multi-index test platform. The results show that the average efficiencies of the optimized drive and energy system are increased by 0.38 % and 3.82 %, the total energy consumption (total hydrogen consumption) is reduced by 25.40 % and 15.39 %, respectively. The maximum fault-free operating time is 5.5h, the average traction power is 21.07 kW, and the average traction force is 10610 N in the all-wheel drive mode. The experimental results fully demonstrate that the electric tractor with the fuel cell distributed drive system as the core can meet the verification of multiple indicators. This study can provide a new theoretical basis and technical verification method for the optimal design and system control of fuel cell distributed drive electric tractors.