NSGA-II Algorithm Research Articles

Multi-objective optimization using improved NSGA-II for integrated process planning and scheduling problems in a machining job shop for large-size valve.

This paper studied an integrated process planning and scheduling problem from a machining workshop for large-size valves in a valve manufacturing plant. Large-size valves usually contain several key parts and are generally produced in small-series production. Almost all the parts need to be manufactured in the same workshop at the same time in the plant. Facilities have to handle various items in one order, including different models, sizes, and types. It is a classical NP-hard problem on a large scale. An improved NSGA-II algorithm is suggested to obtain satisfactory solutions for makespan and manufacturing costs, which involve large optimization parameters and interactions. A two-section encoding method and an inserting greedy decoding method are chosen to enable the algorithm. The dynamic population update strategy based on dynamic population update and the adaptive mutation technique depending on the population entropy changing rate are selected for enhancing both the solution quality and population diversity. The methodology was successfully implemented in a real-life case at a major valve machining workshop operated by Yuanda Valve Company in China. By taking into account realistic factors and restrictions that have been identified from a real-world manufacturing setting, this technique aids in bridging the knowledge gap between present IPPS research and practical valve production implementations.

Multi-Objective Optimization for Complex Systems Considering Both Performance Optimality and Robustness

As engineering systems become increasingly complex, performance requirements rise, and tolerance for design parameter variations becomes more crucial due to increased uncertainty. Tolerance to parameter variation can be measured by the volume of the solution space. A larger solution space implies a higher tolerance to parameter changes and thus greater robustness. The box-shaped solution space, represented by intervals with respect to each design parameter, has the advantage of showing which design parameters can be decoupled. Therefore, this paper proposes a new multi-objective optimization problem to optimize both the performance and volume of the box-shaped solution space simultaneously. Often, optimal performance and maximum volume are conflicting objectives, indicating a trade-off between performance and robustness. Furthermore, the DIRECT-NSGA-II approach is proposed for solving this multi-objective optimization problem. The DIRECT algorithm evaluates the minimum value of the performance function within the box-shaped solution space, while the NSGA-II algorithm identifies Pareto-optimal solution spaces. Finally, two case studies are implemented to illustrate the effectiveness of the DIRECT-NSGA-II method. We can conclude that (I) the proposed DIRECT-NSGA-II approach is suitable for black-box performance functions, (II) any point within the obtained solution space is a good design point, and (III) the proposed optimization problem considers both performance optimality and robustness, enabling the identification of a representative set of Pareto-optimal solution spaces that balance these two factors.

Multi-objective optimization of efficient liquid cooling-based battery thermal management system using hybrid manifold channels

Maintaining a battery cell at an optimal temperature improves both its performance and lifespan. This study proposes a cold plate equipped with hybrid manifold channels, positioned at the bottom of a high-capacity 280 Ah LiFeO4 battery pack. Based on the developed whole battery pack model, the response surface method elucidates the functional relationship between design parameters (i.e., the width of parallel channels, the width of manifold channels, the height of parallel channels, and the inlet velocity) and responses (i.e., the flow pressure drop, the temperature difference of the entire battery modules, and the temperature difference of the cold plate). Multi-objective optimization of design parameters is performed to search the Pareto front to maximize thermal performance and minimize flow pressure drop, employing the NSGA-II algorithm. Results reveal that the maximum battery temperature can be limited to 30.73–33.78 °C with a coolant pressure drop ranging from 7.66 kPa to 1.76 kPa, at a heating power of 10 kW/m3 for the battery cell. The optimal design configuration, identified through TOPSIS, limits the maximum battery temperature to an acceptable temperature of 45 °C at a discharging rate of 3C, with a pressure drop below 4.2 kPa. Compared to the 280 Ah LiFeO4 battery with natural air cooling and forced flow immersion cooling systems, the maximum battery temperature with a discharging rate of 1C is reduced by 17.6 °C and 11.7 °C, respectively.

A stochastic approach for the solution of single and multi–objective optimisation problems of biological processes in sequencing batch reactor

This paper investigates the impact of implementing single and multi-optimisation solutions on the biological treatment process in a sequencing batch reactor (SBR). The research is based on a case study of the water resource recovery facility (WRRF) in Swarzewo, Northern Poland. The paper introduces the adaptive extremum seeking control (ESC) method for dissolved oxygen (DO) concentration control and places it in a layered control structure. Further, it presents the introduction of an optimisation layer for the structure and parameters of the SBR cycle, through the synthesis of stochastic methods: single-objective optimisation (SOO) using a genetic algorithm (GA) and multi-objective optimisation (MOO) using the NSGA-II algorithm. The results were compared to a classical approach with fixed cycle parameters. The paper shows the advantages of optimising cycle parameters, including the number of phases as well as the DO value, on the process flow. These control structures underwent simulation tests in the MATLAB environment with the Simba package. The biochemical processes occurring in the reactor are based on the Activated Sludge Model No. 2d (ASM2d). The optimising control system demonstrates tangible improvements in operational efficiency and significant reductions in electrical energy consumption, highlighting the effectiveness of the proposed methodologies. © 2017 Elsevier Inc. All rights reserved.

Cloud Manufacturing Service Composition Optimization Based on Improved Chaos Sparrow Search Algorithm with Time-Varying Reliability and Credibility Evaluation

The economic friction and political conflicts between some countries and regions have made multinational corporations increasingly focus on the reliability and credibility of manufacturing supply chains. In view of the impact of poor manufacturing entity reliability and service reputation on the new-era manufacturing industry, the time-varying reliability and time-varying credibility of cloud manufacturing (CMfg) services were studied from the perspective of combining nature and society. Taking time-varying reliability, time-varying credibility, composition complexity, composition synergy, execution time, and execution cost as objective functions, a new six-dimension comprehensive evaluation model of service quality was constructed. To solve the optimization problem, this study proposes an improved chaos sparrow search algorithm (ICSSA), where the Bernoulli chaotic mapping formula was introduced to improve the basic sparrow search algorithm (BSSA), and the position calculation formulas of the explorer sparrow and the scouter sparrow were enhanced. The Bernoulli chaotic operator changed the symmetry of the BSSA, increased the uncertainty and randomness of the explorer sparrow position in the new algorithm, and affected the position update and movement strategies of the follower and scouter sparrows. The asymmetric chaotic characteristic brought better global search ability and optimization performance to the ICSSA. The comprehensive performance of the service composition (SvcComp) scheme was evaluated by calculating weighted relative deviation based on six evaluation elements. The WFG and DTLZ series test functions were selected, and the inverse generation distance (IGD) index and hyper volume (HV) index were used to compare and evaluate the convergence and diversity of the ICSSA, BSSA, PSO, SGA, and NSGA-III algorithms through simulation analysis experiments. The test results indicated that the ICSSA outperforms the BSSA, PSO, SGA, and NSGA-III in the vast majority of testing issues. Finally, taking disinfection robot manufacturing tasks as an example, the effectiveness of the proposed CMfg SvcComp optimization model and the ICSSA were verified. The case study results showed that the proposed ICSSA had faster convergence speed and better comprehensive performance for the CMfg SvcComp optimization problem compared with the BSSA, PSO, SGA, and NSGA-III.

Capacity optimization and multimode operation analysis of electric vehicle charging system powered by grid, solar and geothermal energy

The high penetration of renewable energy in electric vehicle (EV) charging system is critical for the EV industrial promotion and carbon neutrality in transportation area. In this study, an EV charging station powered by grid, solar photovoltaic (PV) and geothermal energy from abandoned wells for a highway is proposed together with novel multi-mode operation strategies. Based on the EV charging loads predicted from highway traffic information, NSGA-II algorithm is used for capacity optimization, and two control strategies (i.e., geothermal power multistep output regulation, and geothermal power multistep output + flexible control in charging load regulation) are proposed to achieve the multimode operation. The matching relationship between energy supply and charging load under the proposed and conventional control strategies are compared. The results show that the proposed control strategies allow the optimization of geothermal operation by avoiding the frequent ON/OFF of geothermal power block and increasing its full-generation duration. As a result, the corresponding shares of renewable energy can have 14.8 % and 20.2 % increases, and operation costs are reduced by 14.4 % and 20.9 %, respectively. This study helps to promote the rational configuration of renewable energy-powered charging system for EV and enhance the low-carbon transportation infrastructure.

Analysis and Optimization of a Novel Compact Compliant 2-DOF Positioner for Positioning to Assess Bio-Specimen Characteristics

A novel compact 2-DOF compliant positioner is developed for the purpose of achieving good characteristics such as high natural frequency, high displacement amplification ratio, good linear motion, and compact structure based on its symmetrical structure. To be specific, the developed stage is proposed according to an advanced six-lever displacement amplifier arranged at an inclination angle of the rigid bar utilizing right circular hinges and a parallel guiding mechanism with integrated flexure leaf hinges to attain the above-mentioned characteristics and reduce the decoupling mobility error. First, to quickly assess the initial quality response, an integration method of kinetostatic analysis, the Lagrange method, and finite element analysis was applied to evaluate and verify the quality characteristic of the stage. The experimental result showed that the error between the analytical method and the FEA method was 1.3%, which was relatively small and reliable for quickly assessing the primary quality response of the proposed positioner. Next, to boost the important output characteristics of the developed positioner, the integration approach of the response surface method and NSGA-II algorithm was utilized to find the optimal design variables. Finally, a prototype was manufactured based on the CNC milling method to validate the experimental and FEA analysis results. The attained results show that the optimal results of safety factor and output displacement were 2.4025 and 248.9 µm. Moreover, the FEA verification results were 2.4989 and 242.16 µm, with errors for safety factor and output displacement between the optimal result and the FEA result of 3.86% and 2.78%, respectively. In addition, the simulation and experimental results of the first natural frequency were 371.83 Hz and 329.59 Hz, respectively, and the error between the FEA result and experimental result for the first natural frequency was 11.36%. Furthermore, the achieved results show that the relationship between input displacement and output displacement of the experimental result and the FEA result of the developed structure achieved a good linear connection. These results suggest that the proposed positioner will be a potential structure employed in precise positioning systems and nanoindentation testing positioning systems for checking bio-specimens’ behaviors.

Cooperative task allocation method for air-sea heterogeneous unmanned system with an application to ocean environment information monitoring

Ocean environment information monitoring is crucial to maritime battlefield situation awareness, marine disaster prevention, and marine resources utilization. However, current monitoring means, including remote sensing satellites, scientific investigation ships, and ocean observation stations, have limitations in terms of spatial coverage, spatiotemporal resolution, and manpower resources. Therefore, this study proposed the Air-Sea heterogeneous unmanned system composed of UAVs and USVs to carry out ocean environment information monitoring tasks.) Firstly, an algorithm named FASO (Firefly Algorithm Based Particle Swarm Optimization) is proposed to obtain the deployment of monitoring nodes in the sea-surface and airspace. Secondly, a multi-objective optimization cooperative task allocation model is established, which contains complex constraints and takes the energy consumption cost of UAVs and USVs as optimization goals. Finally, a cooperative task allocation method based on the improved NSGA-II algorithm is presented to enhance the convergence speed and quality of the cooperative task allocation for air-sea heterogeneous unmanned system. Simulation results demonstrate that the proposed algorithm have higher generality and superiority compared to other algorithms. This study provides a specific approach for the application of heterogeneous unmanned systems in the field of ocean monitoring.

Multi-objective optimization of ternary geopolymers with multiple solid wastes

The design of the mixtures of ternary geopolymers is challenging due to the need to balance multiple objectives, including cost, strength, and carbon emissions. In order to address this multi-objective optimization (MOO) problem, machine learning models and the NSGA-II algorithm are employed in this study. To train the machine learning models, namely Artificial Neural Network (ANN), Support Vector Regressor, Extremely Randomized Tree, and Gradient Boosting Regression, 120 mixtures with uniaxial compressive strength (UCS) values of ternary geopolymers with fly ash (FA), granulated blast furnace slag (GBFS) and steel slag (SS) as precursor materials were obtained from laboratory tests. Results show that the ternary geopolymer with the ratio of FA:GBFS:SS of 2:5:3 has the highest 28-d UCS of 46.8 MPa. The predictive accuracy of the ANN model is the highest with R = 0.949 and RMSE=3.988 MPa on the test set. Furthermore, the Shapley Additive Explanations analysis indicates that precursor materials exhibit the most significant influence on the UCS, with the content of GBFS being particularly influential. Based on the ANN model and NSGA-II algorithm, a multi-objective optimization (MOO) model is developed to optimize simultaneously the strength, cost and carbon emission of the ternary geopolymer. The derived MOO model can be used to design mixtures of other cementitious materials with multiple objectives.

Multi-objective optimization of tribological properties of camshaft bearing pairs using DNN coupled with NSGA-II algorithm and TOPSIS

PurposeThis study aims to propose an optimization framework using deep neural networks (DNN) coupled with nondominated sorting genetic algorithm II and technique for order preference by similarity to an ideal solution method to improve the tribological properties of camshaft bearing pairs of internal combustion engine.Design/methodology/approachA lubrication model based on the theory of elastohydrodynamic lubrication and flexible multibody dynamics was developed for a V6 diesel engine. Setting DNN model as fitness function, the multi-objective optimization genetic algorithm and decision-making method were used to optimize the bearing pair structure with the goal of minimizing the total friction loss and the difference of the average values of minimum oil film thickness.FindingsThe results show that the lubrication state corresponding to the optimized bearing pair structure is elastohydrodynamic lubrication. Compared with the original structure, the optimized structure significantly reduces the total friction loss.Originality/valueThe optimized performance and corresponding structural parameters are obtained, and the optimization results were verified through multibody dynamics simulation.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-12-2023-0417/

Multi-objective integrated decision method of cascade hydropower stations based on optimization algorithm and evaluation model

The establishment and functioning of cascade hydropower stations have significantly altered the natural state of rivers, leading to increasingly severe ecological impacts downstream. To mitigate the adverse effects of cascade reservoir impoundment on river ecosystems and achieve the multi-objective goals of hydropower development and environment protection, this study presents an integrated decision model for optimizing the operation of cascade hydropower stations, utilizing the lower Jinsha River as a case illustration. The proposed model comprises three main components: ecological flow calculation, establishment of a multi-objective optimization model, and scheme evaluation. The ecological flow threshold for the river was determined using the Tennant method. The multi-objective optimization model was formulated with objectives including power generation, ecological flow shortage, and navigation disruption days. The NSGA-II algorithm was employed to resolve the optimization framework. The improved TOPSIS method was adopted for evaluating the operation schemes. The case study conducted in the downstream region of the Jinsha River revealed significant conflicts between the objectives of power generation and ecological conservation. Moreover, different typical years exerted a considerable influence on determining the optimal operation scheme. The proposed integrated decision model provides a scientific framework for guiding the water resource dispatching of cascade hydropower stations and achieving the harmonization of economic and ecological benefits. Overall, this research contributes to the academic field by addressing the complex difficulties related to optimizing the operation of cascade hydropower stations while considering ecological concerns. The findings underscore the importance of adopting a comprehensive decision-making approach that integrates multiple objectives and utilizes advanced algorithms for sustainable river management.

Super-agile satellites imaging mission planning method considering degradation of image MTF in dynamic imaging

Super-agile satellites with dynamic imaging capabilities can significantly improve the imaging efficiency of space-borne Earth observation. However, the image modulation transfer function (image MTF) of dynamic imaging varies dramatically with changes in the attitude maneuver status while improving observation efficiency. Therefore, both the observation efficiency and image MTF should be considered in the imaging mission planning of super-agile satellites. Existing imaging mission planning methods are mainly aimed at traditional agile satellites that are not capable of dynamic imaging, and the mainly optimization objectives are coverage revenue, spatial resolution, and task performance time, without considering the observation efficiency and image MTF as the optimization objectives. To overcome the problem of image MTF degradation caused by dynamic imaging, a mission planning method which considers the degradation of the image MTF and observation efficiency was proposed. Firstly, a mapping relationship between the angular velocity of the attitude maneuvering and the image MTF was constructed. On this basis, a Pareto multi-objective imaging mission planning model of partial order based was established. Considering the particularity of the model, the non-dominated sorting strategy of the NSGA-II algorithm was redesigned when solving the model using NSGA-II algorithm. Finally, the imaging tasks of different scales in single satellite and multi-satellite simulation scenes were set, and the proposed imaging mission planning method was verified. The planning results showed that the proposed method can achieve a balanced optimization of the image MTF and task efficiency under the premise of ensuing maximum observation coverage revenue.

Optimization design of crashworthiness of polyurethane foam filled Origami thin wall square tube based on an surrogate model

Rigid polyurethane foam, due to its low density, light weight and effective energy absorption, can be used as a new filling material to improve the energy absorption capacity of pipe fittings, thus improving the overall crashworthiness of automobiles. In this paper, numerical simulations on rigid polyurethane foam-filled origami thin-walled pipe fittings are carried out. The effects of interlayer dihedral angle, number of layers of creased pipe, and filling of foam with different densities on the crashworthiness of the pipe fittings are compared and analyzed. Additionally, an surrogate model is established to predict the correlation between the above three factors and the model. Taking the specific absorption energy (SEA) and the initial peak force (PCF) as optimization objectives, the NSGA-II algorithm and the CHC algorithm were used for optimization analysis respectively. Simulation experiments were conducted to validate the accuracy of the optimized structural parameters. The results show that the CHC algorithm provides more accurate optimization results compared to the NSGA-II algorithm. The optimized optimal interlayer dihedral angle is 150°, the number of creased tube layers is 8, and the foam density is 0.3 g/cm3. Consequently, the tubing’s specific absorption energy is enhanced by 10.3 % compared to ordinary straight tubing, demonstrating the significant improvement in crashworthiness achieved through foam filling. This study proves that the filling of tubing with foam has excellent impact resistance, and the results provide a certain reference value for future research.

Crashworthiness analysis and optimisation of a multi-level bionic multi-cell tube under axial dynamic impact

In this study, a multi-level bionic multi-cell tube (MBMT-n) is proposed, inspired by the horsetail’s cross-sectional structure, and the multi-cell tube shape and distribution characteristics of bamboo vascular bundles. A systematic crashworthiness analysis of MBMT-n under axial dynamic impact was conducted using LS-DYNA. The findings indicate that MBMT-n can enhance crashworthiness while maintaining the same mass. Additionally, the impact of wall thickness and bionic unit cell’s length–width ratio was investigated. Furthermore, a theoretical prediction model for mean crushing force (MCF) was established using the simplified super-folding unit theory. Lastly, to further enhance the structure’s crashworthiness, multi-objective optimisation of MBMT-3 was performed using the NSGA-II algorithm. The several optimal structures were subsequently identified under various peak crushing force (PCF). This study offers a novel approach to the design of energy-absorbing structures.

Uniform or demand-driven allocation? Optimal management of social donations distribution in response to sudden outbreaks

PurposeThis study aims to address challenges in the distribution of social donations during epidemic emergencies, focusing on issues such as uneven distribution and material stacking. The goal is to propose optimized strategies that enhance equity and efficiency in the allocation of donated resources.Design/methodology/approachFirstly, the satisfaction function is constructed from two perspectives of the designated hospital and the Red Cross. On this basis, the fairness perception level of the two is portrayed. Then, we set the time weights, and construct a multi-objective programming model by combining the resource constraints in the social donation distribution process. The combined algorithm of NSGA-II and TOPSIS is also designed for model solving. Finally, an example of social donation distribution of the Red Cross Society of China Wuhan Branch is conducted for numerical analysis.FindingsNumerical analysis reveals that timely transmission of demand information favors a demand-oriented distribution strategy for optimal efficiency. However, in scenarios with poor demand information transmission, an equal distribution of social donations proves to be a more effective strategy. Equal distribution ensures rapid allocation while minimizing perceived unfairness at designated hospitals, ultimately improving overall satisfaction levels and emergency response effectiveness.Practical implicationsThe findings provide practical insights for emergency response planners. These include translating the developed methods into guiding principles, establishing real-time monitoring systems, enhancing training for relevant departments, and implementing evaluation mechanisms. Practitioners can utilize this knowledge to optimize the efficiency of social donation distribution during sudden outbreaks.Social implicationsThe equitable distribution of social donations ensures efficient resource allocation and minimizes perceived unfairness, contributing to improved social satisfaction levels. This has broader implications for community resilience and support during emergencies.Originality/valueThis research contributes to the field by proposing a comprehensive model for optimizing social donation distribution in emergencies. The integration of fairness perception, time weights, and a multi-objective planning approach, along with the application of the combined algorithm of NSGA-II and TOPSIS, adds novelty and practical value to the existing literature. The study serves as a decision-making reference for enhancing emergency response theories in sudden event.

Enhancing P2/O3 Biphasic Cathode Performance for Sodium-Ion Batteries: A Metaheuristic Approach to Multi-Element Doping Design.

Sodium-ion batteries (SIBs) have emerged as a compelling alternative to lithium-ion batteries (LIBs), exhibiting comparable electrochemical performance while capitalizing on the abundant availability of sodium resources. In SIBs, P2/O3 biphasic cathodes, despite their high energy, require furthur improvements in stability to meet current energy demands. This study introduces a systematic methodology that leverages the meta-heuristically assisted NSGA-II algorithm to optimize multi-element doping in electrode materials, aiming to transcend conventional trial-and-error methods and enhance cathode capacity by the synergistic integration of P2 and O3 phases. A comprehensive phase analysis of the meta-heuristically designed cathode material Na0.76Ni0.20Mn0.42Fe0.30Mg0.04Ti0.015Zr0.025O2 (D-NFMO) is presented, showcasing its remarkable initial reversible capacity of 175.5mAh g-1 and exceptional long-term cyclic stability in sodium cells. The investigation of structural composition and the stabilizing mechanisms is performed through the integration of multiple characterization techniques.Remarkably, the irreversible phase transition of P2→OP4 in D-NFMO is observed to be dramatically suppressed, leading to a substantial enhancement in cycling stability. The comparison with the pristine cathode (P-NFMO) offers profound insights into the long-term electrochemical stability of D-NFMO, highlighting its potential as a high-voltage cathode material utilizing abundant earth elements in SIBs. This study opens up new possibilities for future advancements in sodium-ion battery technology.

Multi-objective optimization and evaluation of the building-integrated photovoltaic/thermal-energy pile ground source heat pump system

Building integrated photovoltaic thermal (BIPV/T)-energy pile ground source heat pump (GSHP) system effectively maintains the soil thermal balance and improves the photovoltaic efficiency by recovering the waste solar heat from the BIPV/T collector to charge the ground. However, due to the strict carbon emission restriction and economic consideration for system application, multi-objective optimization should be implemented for the system design to further enhance its overall performance. Therefore, a complete optimization framework is proposed in this paper, which is based on the NSGA-II algorithm and functions by coupling TRNSYS and jEPlus+EA. Using the proposed optimization frame, a comprehensive optimization is conducted for the system, which optimizes five main design variables and adopts the total power consumption (TPC), life cycle climate performance (LCCP), and life cycle cost (LCC) to reflect the system's energetic, environmental, and economic performances. Seven optimization cases are included in the optimization project to identify design schemes that meet all possible proposed performance requirements for the system application. The optimized results show that the system optimization improves its performance greatly compared to the design condition, with a range of 38.03% ∼ 50.05% for the TPC, 24.52% ∼ 25.86% for LCCP, and 56.74% ∼ 61.66% for LCC. The energetic, environmental and economic performances of the system can be maximized by minimizing the TPC to 357.29 MWh in Case 1, LCCP to 596.15 tCO2 in Case 2, and LCC to 17.03 × 104 CNY in Case 3. By breaking down the objective functions, it is found that the heat pump unit holds paramount importance in all aspects of system performance, and the BIPV/T collector proves advantageous in enhancing the system's long-term performance due to its massive electricity generation. Accordingly, one important finding is that it is advisable to incorporate as many BIPV/T collectors as feasible while maintaining soil heat balance. The optimal energy pile number varies among the seven optimization cases, resulting in different heating capacities for the heat pump unit. Research outcomes in this work offer references for designing the energy-pile GSHP system to enhance the penetration of renewable energy into buildings.

Performance analysis of four-stage thermoelectric cooler for focal plane infrared detectors

A cooler provides a low-temperature working environment for refrigerated infrared detection chips and is an indispensable component in infrared detectors that ensures their smooth operation. In this paper, a thermodynamic model of a heat-pipe-type four-stage thermoelectric cooler with a large temperature difference applied to a focal plane infrared detector is constructed. The heat leakage of the detector is calculated using the model. The performance indexes of the coordination performance coefficient and module utilization coefficient are used to unify the cooling capacity and economic performance of the device. The impression of the working current, heat pipe parameters, and cross-sectional area of the thermoelectric leg on the performance of cooler are analyzed. A Pareto optimal solution is obtained using an NSGA-II algorithm and TOPSIS decision. A low cooling temperature is obtained using a multistage thermoelectric cooler to achieve a large cooling temperature difference. When the working current is 5.65 A and the cross-sectional area is 4.5 mm2, the maximum cooling temperature difference reaches 110.6 °C, and the lowest cooling temperature reaches -83.6 °C. When the working temperature of the detector is -60 °C, the current and cross-sectional area corresponding to the optimal solution of cooling capacity-COP-power consumption are 1.51 A and 2.74 mm2, respectively.

Two-Stage Optimal Design Method for Asymmetric Base-Isolated Structures Subject to Pulse-Type Earthquakes

Asymmetric base-isolated structures subjected to severe torsion may suffer further aggravation of their torsional and translational responses under pulse-type earthquakes. To counteract these detrimental impacts, this study introduces a two-stage optimal design method. The first stage involved the application of the NSGA-II algorithm for determining an optimal isolator arrangement—namely, position and category—with the objective of reducing both the maximum interstory rotation of the superstructure and the isolation layer. In the second stage, the inclusion of viscous dampers served to minimize the excessive translational response triggered by pulse-type earthquakes. The influence of these dampers’ positions on the structural response was carefully evaluated. The final application of this optimal design method was demonstrated on an asymmetric base-isolated structure. The results indicated a significant reduction in the translational and torsional responses of the asymmetric base-isolated structure when the two-stage optimal design method was utilized, compared to those of structures designed using traditional conceptual methods. It was found that by installing viscous dampers in the isolation layer along both the x and the y directions—specifically, underneath the mass center of the superstructure (CMS)—the effectiveness of the torsional resistance from the first stage could be effectively maintained.

Modeling and Optimization of the Air-Supported Membrane Coal Shed Structure in Ports

The air-supported membrane coal shed is widely used in bulk cargo terminals. It not only effectively protects goods from adverse weather conditions but also helps reduce coal dust and harmful gas emissions, promoting the green and sustainable development of ports. However, in practical engineering, the design parameters of the coal shed are often based on experience, making it difficult to accurately assess the quality of the structural design. The flexibility of the membrane material also makes the structure susceptible to deformation or tearing. This paper mainly focuses on modeling and solving the optimization design issues of air-supported membrane coal shed structures. According to the evaluation criteria for the form of air-supported membrane coal sheds, a multi-objective structural optimization model is established to minimize the maximum stress on the membrane surface, ensure uniform stress distribution, maximize structural stiffness, and minimize costs. The study utilizes a combined optimization approach using ANSYS 19.0 and MATLAB 2016a, incorporating an improved NSGA-II algorithm program developed in MATLAB into ANSYS for structural form analysis and load calculation. The research results indicate that the optimal solution reduces the maximum stress on the loaded membrane surface by 5.36%, shortens the maximum displacement by 30.3%, and saves on economic costs by 9.85%. Compared to traditional empirical design methods, the joint use of MATLAB and ANSYS for optimization design can provide more superior solutions, helping ports to achieve environmental protection and economic efficiency goals.