Static and dynamic collaborative optimization of ship hull structure

The goal of this effort was to provide a static and dynamic collaborative optimization （CO） model for the design of ship hull structure. The CO model integrated with static, mode and dynamic analyses. In the system-level optimization model, a new objective function was advised, integrating all the subsystem-levels＇ objective functions, so as to eliminate the effects of dimensions and magnitude order. The proposed CO architecture enabled multi-objectives of the system and subsystem-level to be considered at both levels during optimization. A bi-level optimization strategy was advised, using the multi-island genetic algorithm. The proposed model was demonstrated with a deck optimization problem of container ship stern. The analysis progress and results of example show that the CO strategy is not only feasible and reliable, but also well suited for use in actual optimization problems of ship design.

Application of the optimal Latin hypercube design and radial basis function network to collaborative optimization

Improving the efficiency of ship optimization is crucial for modem ship design. Compared with traditional methods, multidisciplinary design optimization （MDO） is a more promising approach. For this reason, Collaborative Optimization （CO） is discussed and analyzed in this paper. As one of the most frequently applied MDO methods, CO promotes autonomy of disciplines while providing a coordinating mechanism guaranteeing progress toward an optimum and maintaining interdisciplinary compatibility. However, there are some difficulties in applying the conventional CO method, such as difficulties in choosing an initial point and tremendous computational requirements. For the purpose of overcoming these problems, optimal Latin hypercube design and Radial basis function network were applied to CO. Optimal Latin hypercube design is a modified Latin Hypercube design. Radial basis function network approximates the optimization model, and is updated during the optimization process to improve accuracy. It is shown by examples that the computing efficiency and robustness of this CO method are higher than with the conventional CO method.

Collaborative optimization of maintenance and spare ordering of continuously degrading systems

A collaborative optimization model for maintenance and spare ordering of a single-unit degrading system is proposed in this paper based on the continuous detection. A gamma distribution is used to model the material degradation. The degrading decrement after the imperfect maintenance action is assumed as a random variable normal distribution. This model aims to ob- tain the optimal maintenance policy and spare ordering point with the expected cost rate within system lifecycle as the optimization objective. The rationality and feasibility of the model are proved through a numerical example.

Collaborative optimization model of renewable energy development considering peak shaving costs of various flexibility resources

China has set carbon emission goals for 2030 and 2060.Renewable energy sources,primarily wind and photovoltaic power,are being considered as the future of power generation.The major limitation to the development of new energies is the limited flexibility of regulations on power system resources,resulting in insufficient consumption capacity.Thus,the flexible resource costs for peak shaving as well as the reasonable coordinated development and operation optimization of regional renewable energy need to be considered.In this study,a renewable energy development layout configuration analysis method was established by considering the composite cost of a power system,comprehensively analyzing the potential of various flexibility regulation resources for the power system and its composite peak shaving cost,and combining renewable energy output characteristics,load forecasting,grid development,and other factors.For the optimization of various flexible resource utilization methods,a peak shaving cost estimation method from the perspective of the entire power system was established by combining the on-grid electricity prices and operating costs of different power sources.A collaborative optimization model of power system operation that aims at the lowest peak shaving cost and satisfies the constraints of operation,safety,and environmental protection was proposed.Finally,a certain area of Gansu Province was used as an example to perform detailed analysis and calculation,which demonstrated that the model has an optimal effect.This model can provide an analysis method for regional renewable energy development layout configurations and system optimization operations.

Damping Collaborative Optimization of Five-suspensions for Driver-seat-cab Coupled System

Both the seat and cab system of truck play a vital role in ride comfort.The damping matching methods of the two systems are studied separately at present.However,the driver,seat,and cab system are one inseparable whole.In order to further improve ride comfort,the seat suspension is regarded as the fifth suspension of the cab,a new idea of ＂Five-suspensions＂ is proposed.Based on this idea,a 4 degree-of-freedom driver-seat-cab coupled system model is presented.Using the tested cab suspensions excitations as inputs and seat acceleration response as compared output,the simulation model is built.Taking optimal ride comfort as target,a new method of damping collaborative optimization for Five-suspensions is proposed.With a practical example of seat and cab system,the damping parameters are optimized and validated by simulation and bench test.The results show the seat vertical frequency-weighted RMS acceleration values tested for the un-optimized and optimized Five-suspensions are 0.50 m/s～2 and 0.39 m/s～2,respectively,with a decrease by 22.0%,which proves the model and method proposed are correct and reliable.The idea of ＂Five-suspensions＂ and the method proposed provide a reference for achieving global optimal damping matching of seat suspension and cab suspensions.

Bilateral Collaborative Optimization for Cloud Manufacturing Service

Manufacturing service composition of the supply side and scheduling of the demand side are two important components of Cloud Manufacturing,which directly affect the quality of Cloud Manufacturing services.However,the previous studies on the two components are carried out independently and thus ignoring the internal relations and mutual constraints.Considering the two components on both sides of the supply and the demand of Cloud Manufacturing services at the same time,a Bilateral Collaborative Optimization Model of Cloud Manufacturing(BCOM-CMfg)is constructed in this paper.In BCOM-CMfg,to solve the manufacturing service scheduling problem on the supply side,a new efficient manufacturing service scheduling strategy is proposed.Then,as the input of the service composition problem on the demand side,the scheduling strategy is used to build the BCOM-CMfg.Furthermore,the Cooperation Level(CPL)between services is added as an evaluation index in BCOM-CMfg,which reveals the importance of the relationship between services.To improve the quality of manufacturing services more comprehensively.Finally,a Self-adaptive Multi-objective Pigeon-inspired Optimization algorithm(S-MOPIO)is proposed to solve the BCOM-CMfg.Simulation results show that the BCOM-CMfg model has advantages in reliability and cost and S-MOPIO can solve BCOM-CMfg effectively.

Design of hydraulic shift mechanism for AMT vehicles based on the collaborative optimization method

Based on multidisciplinary design optimization（MDO）,a new design method is put forward for hydraulic shift mechanism of heavy-duty vehicle automated manual transmission（AMT）.Taking a shift cylinder for example,the collaborative optimization（CO）method for the design problem of a cylinder is devided into one system level design optimization problem and three subsystem level design optimization problems.The system level is an economic model and the subsystem level is mechanics,kinetics,and a reliability model.Application of the multidisciplinary design optimization software iSIGHT modeling and solving,optimal solution of the shifting cylinder CO model is obtained.According to the optimal solution,oil cylinders are machined out and installed on the gearbox of an AMT system for the bench cycle shift test.The results show that the output force and action speed of the optimized mechanism can meet requirements very well.In addition,the optimized mechanism has a better performance compared to the structure of the traditional design method,which indicates that the CO method can optimize the design of hydraulic transmission.

Force-position collaborative optimization of rope-driven snake manipulator for capturing non-cooperative space targets

With the rapid development of space activities,non-cooperative space targets increase swiftly,such as failed satellites and upper stages,threating normal spacecrafts seriously.As there are some problems in the capture process,such as excessive collision and fast tumbling of targets,manipulator with redundant Degrees of Freedom(DOFs)can be used to improve the compliance and therefore solve these problems.The Rope-Driven Snake Manipulator(RDSM)is a combina-tion of hyper-redundant DOFs and better compliance,and therefore it is suitable for capturing mis-sion.In this paper,a snake manipulator mechanism is designed,and the complete kinematic model and system dynamic model considering RDSM,target and contact is established.Then,to obtain the configuration of joint with hyper-redundant DOFs,an improved motion dexterity index is pro-posed as the joint motion optimization target.Besides,the force-position collaborative optimization index is designed to adjust active stiffness,and the impedance control method based on the modified index is used to capture the space target.Finally,the proposed force-position collaborative opti-mization method is verified by virtual prototype co-simulation.The results demonstrate that based on the proposed method,the collision force is reduced by about 25%compared to normal impe-dance control,showing higher safety.

Mathematical Modeling and a Multiswarm Collaborative Optimization Algorithm for Fuzzy Integrated Process Planning and Scheduling Problem

Considering both process planning and shop scheduling in manufacturing can fully utilize their complementarities,resulting in improved rationality of process routes and high-quality and efficient production. Hence,the study of Integrated Process Planning and Scheduling (IPPS) has become a hot topic in the current production field. However,when performing this integrated optimization,the uncertainty of processing time is a realistic key point that cannot be neglected. Thus,this paper investigates a Fuzzy IPPS (FIPPS) problem to minimize the maximum fuzzy completion time. Compared with the conventional IPPS problem,FIPPS considers the fuzzy process time in the uncertain production environment,which is more practical and realistic. However,it is difficult to solve the FIPPS problem due to the complicated fuzzy calculating rules. To solve this problem,this paper formulates a novel fuzzy mathematical model based on the process network graph and proposes a MultiSwarm Collaborative Optimization Algorithm (MSCOA) with an integrated encoding method to improve the optimization. Different swarms evolve in various directions and collaborate in a certain number of iterations. Moreover,the critical path searching method is introduced according to the triangular fuzzy number,allowing for the calculation of rules to enhance the local searching ability of MSCOA. The numerical experiments extended from the well-known Kim benchmark are conducted to test the performance of the proposed MSCOA. Compared with other competitive algorithms,the results obtained by MSCOA show significant advantages,thus proving its effectiveness in solving the FIPPS problem.

The impact of urban morphology on multiple ecological effects:Coupling relationships and collaborative optimization strategies

Urban morphology significantly affects the ecological effects of urban heat islands,ventilation,and atmospheric pollution.Here,we reveal the mechanisms linking the ecological effects of urban morphology to develop a planning approach for the collaborative optimization of multiple ecological effects.Considering Shenyang,a cold city in northern China,as the study area,a multiple regression model of morphological parameters and ecological effects was established,and the impact of morphological parameters on ecological effects was explored.The results show that the aspect ratio of the streets,building density,and vegetation coverage are sensitive to multiple ecological effects.The inflection point of the ecological effect function curve occurs when the aspect ratio of the building and building density are 0.2 and 0.3,respectively.In addition,for optimal design applications in typical areas of the city,to obtain a Pareto-optimal urban morphology,Grasshopper is used to establish a parametric platform,wherein a genetic algorithm solves the multiple regression equation set.Ultimately,five ecological effect indicators are optimized and show 8.4%,5.0%,31.6%,33.1%,and 12.5%improvement.The study effectively constructs a collaborative optimization planning and design method for multiple ecological effects.

Multi-Stage Multidisciplinary Design Optimization Method for Enhancing Complete Artillery Internal Ballistic Firing Performance

To enhance the comprehensive performance of artillery internal ballistics—encompassing power,accuracy,and service life—this study proposed a multi-stage multidisciplinary design optimization(MS-MDO)method.First,the comprehensive artillery internal ballistic dynamics(AIBD)model,based on propellant combustion,rotation band engraving,projectile axial motion,and rifling wear models,was established and validated.This model was systematically decomposed into subsystems from a system engineering perspective.The study then detailed the MS-MDO methodology,which included Stage I(MDO stage)employing an improved collaborative optimization method for consistent design variables,and Stage II(Performance Optimization)focusing on the independent optimization of local design variables and performance metrics.The methodology was applied to the AIBD problem.Results demonstrated that the MS-MDO method in Stage I effectively reduced iteration and evaluation counts,thereby accelerating system-level convergence.Meanwhile,Stage II optimization markedly enhanced overall performance.These comprehensive evaluation results affirmed the effectiveness of the MS-MDO method.

Multi-Objective Optimization of Aluminum Alloy Electric Bus Frame Connectors for Enhanced Durability

The widespread adoption of aluminumalloy electric buses,known for their energy efficiency and eco-friendliness,faces a challenge due to the aluminum frame’s susceptibility to deformation compared to steel.This issue is further exacerbated by the stringent requirements imposed by the flammability and explosiveness of batteries,necessitating robust frame protection.Our study aims to optimize the connectors of aluminum alloy bus frames,emphasizing durability,energy efficiency,and safety.This research delves into Multi-Objective Coordinated Optimization(MCO)techniques for lightweight design in aluminum alloy bus body connectors.Our goal is to enhance lightweighting,reinforce energy absorption,and improve deformation resistance in connector components.Three typical aluminum alloy connectors were selected and a design optimization platform was built for their MCO using a variety of software and methods.Firstly,through three-point bending experiments and finite element analysis on three types of connector components,we identified optimized design parameters based on deformation patterns.Then,employing Optimal Latin hypercube design(OLHD),parametric modeling,and neural network approximation,we developed high-precision approximate models for the design parameters of each connector component,targeting energy absorption,mass,and logarithmic strain.Lastly,utilizing the Archive-based Micro Genetic Algorithm(AMGA),Multi-Objective Particle Swarm Optimization(MOPSO),and Non-dominated SortingGenetic Algorithm(NSGA2),we explored optimized design solutions for these joint components.Subsequently,we simulated joint assembly buckling during bus rollover crash scenarios to verify and analyze the optimized solutions in three-point bending simulations.Each joint component showcased a remarkable 30%–40%mass reduction while boosting energy absorption.Our design optimization method exhibits high efficiency and costeffectiveness.Leveraging contemporary automation technology,the design optimization platform developed in this study is poised to facilitate intelligent optimization of lightweight metal components in future applications.

Collaborative robust dispatch of electricity and carbon under carbon allowance trading market

The launch of the carbon-allowance trading market has changed the cost structure of the power industry.There is an asynchronous coupling mechanism between the carbon-allowance-trading market and the day-ahead power-system dispatch.In this study,a data-driven model of the uncertainty in the annual carbon price was created.Subsequently,a collaborative,robust dispatch model was constructed considering the annual uncertainty of the carbon price and the daily uncertainty of renewable-energy generation.The model is solved using the column-and-constraint generation algorithm.An operation and cost model of a carbon-capture power plant(CCPP)that couples the carbon market and the economic operation of the power system is also established.The critical,profitable conditions for the economic operation of the CCPP were derived.Case studies demonstrated that the proposed low-carbon,robust dispatch model reduced carbon emissions by 2.67%compared with the traditional,economic,dispatch method.The total fuel cost of generation decreases with decreasing,conservative,carbon-price-uncertainty levels,while total carbon emissions continue to increase.When the carbon-quota coefficient decreases,the system dispatch tends to increase low-carbon unit output.This study can provide important guidance for carbon-market design and the low-carbon-dispatch selection strategies.

Multidiscipline collaborative optimization of differential steering system of electric vehicle with motorized wheels

Based on the multidiscipline design optimization theory, a multidiscipline collaborative optimization model of the differential steering system of electric vehicle with motorized wheels is built, with the steering economy as the main system and the steering road feel, the steering flexibility and the mechanic character of the steering sensors as the subsystems. Considering the coupled relationship of each discipline, the main system is optimized by the multi-island algorithm and the subsystems are optimized by the sequential quadratic programming algorithm. The simulation results show that the steering economy can be optimized by the collaborative optimization, and that the system can get good steering road feel, good steering flexibility and good mechanic character of the steering sensors.

AGENT based structural static and dynamic collaborative optimization

A static and dynamic collaborative optimization mode for complex machine system and itsontology project relationship are put forward, on which an agent-based structural static and dynamiccollaborative optimization system is constructed as two agent colonies: optimization agent colony andfinite element analysis colony. And a two-level solving strategy as well as the necessity and possibilityfor handing with finite element analysis model in multi-level mode is discussed. Furthermore, the coop-eration of all FEA agents for optimal design of complicated structural is studied in detail. Structural stat-ic and dynamic collaborative optimization of hydraulic excavator working equimpent is taken as an ex-ample to show that the system is reliable.

Multi-objective Collaborative Optimization for the Lightweight Design of an Electric Bus Body Frame

To analyze the rollover safety,finite element models were established for the electric bus body frame,rollover simulation platform,living space,and bus rollover.The strength and stiffness of the body frame were calculated under four typical work-ing conditions considering the main low-order elastic modal characteristics.The results indicate that the initial body frame of the electric bus satisfies the required structural strength,stiffness,modes,and rollover safety,and it has great potential for lightweight design.Sensitivity and structural contribution analyses were performed to determine the design variables for lightweight optimization of the body frame,and a mathematical model was established for multi-objective collaborative optimization design of the electric bus.Then,the radial basis function neural network was used to approximate the optimiza-tion model.Besides,the accuracy of the approximate model was verified,and the non-dominated sorting genetic algorithm II was employed to determine solutions for the lightweight optimization.Compared with the initial model,the mass of the optimized model is reduced by 240 kg(9.0%)without any changes in the materials of the body frame.

Improvement of Collaborative Optimization

This paper presents a new improved collaborative optimization (CO) model that provides solution capabilities for multiobjective multidisciplinary optimization problems. Reasons that cause computational difficulties in CO algorithm are firstly analyzed. Then a new system level objective function is advised to minimize relative value between the collaborative objective function and single disciplinary objective function. And it eliminates the effect of dimensions and magnitude orders among objectives. A new subsystem level objective function is developed that includes the disciplinary objective function and the consistency constraint. A new CO framework which is more suitable for multilevel distributed design is advised. In this CO framework,the system level optimizer does not only independently invoke the subdisciplinary analysis tools,but also invoke its subdiscipline optimizer. The improved CO model proposed in this work is demonstrated with two examples. The results of examples show the improved CO is not only feasible,reliable and efficient,but also well suitable to solve the multiobjective optimization problems in multidisciplinary design environment.

Multidisciplinary Modeling and Optimization Method of Remote Sensing Satellite Parameters Based on SysML-CEA

To enhance the efficiency of system modeling and optimization in the conceptual design stage of satellite parameters,a system modeling and optimization method based on System Modeling Language and Co-evolutionary Algorithm is proposed.At first,the objectives of satellite mission and optimization problems are clarified,and a design matrix of discipline structure is constructed to process the coupling relationship of design variables and constraints of the orbit,payload,power and quality disciplines.In order to solve the problem of increasing nonlinearity and coupling between these disciplines while using a standard collaborative optimization algorithm,an improved genetic algorithm is proposed and applied to system-level and discipline-level models.Finally,the CO model of satellite parameters is solved through the collaborative simulation of Cameo Systems Modeler(CSM)and MATLAB.The result obtained shows that the method proposed in this paper for the conceptual design phase of satellite parameters is efficient and feasible.It can shorten the project cycle effectively and additionally provide a reference for the optimal design of other complex projects.

Collaborative optimization of screening efficiency and screen surface load-PartⅠ:Modeling and evaluation

The screen surface load(SSL)caused by granular materials is an important factor affecting the structural performance of vibrating screen.Based on virtual experiment,a multi-objective collaborative optimiza-tion method is proposed to control the SSL under high screening efficiency(SE)in this work.Firstly,a DEM model was established to study the influence of process parameters on SE and SSL.Secondly,the NSGA-Ⅱ(Non-dominated Sorting Genetic Algorithm)was employed to optimize the screening parameters with both SE and SSL as targets.The optimization method proves to be effective implementing on a linear vibrating screening.With SE equals to 98.5%,the SSL optimizable range is 39.2%.While compromising the SE to 88.7%,the SSL optimizable range improves to 48.6%.The result shows that the collaborative optimization could effectively control the SSL while maintaining a high SE,which is of great significance to improve the service life of screen surface and screen body.

Receding-Horizon Trajectory Planning for Under-Actuated Autonomous Vehicles Based on Collaborative Neurodynamic Optimization

This paper addresses a major issue in planning the trajectories of under-actuated autonomous vehicles based on neurodynamic optimization.A receding-horizon vehicle trajectory planning task is formulated as a sequential global optimization problem with weighted quadratic navigation functions and obstacle avoidance constraints based on given vehicle goal configurations.The feasibility of the formulated optimization problem is guaranteed under derived conditions.The optimization problem is sequentially solved via collaborative neurodynamic optimization in a neurodynamics-driven trajectory planning method/procedure.Simulation results with under-actuated unmanned wheeled vehicles and autonomous surface vehicles are elaborated to substantiate the efficacy of the neurodynamics-driven trajectory planning method.