Integrated Optimization Method Research Articles

Integrated optimization of scheduling for unmanned follow-me cars on airport surface

To promote the application of automated vehicles in large airports, in this study, we present an integrated optimization method for scheduling Unmanned follow-me cars. The scheduling process is divided into three phases: Dispatch, Guidance, and Recycle. For the Dispatch phase, we establish a vehicle assignment model, to allocate the vehicle resource equitably. For the Guidance phase, we offer an quantitative way, to measure the spacing between Unmanned follow-me car and aircraft. To optimize the efficiency of airport operation in the three phases and ensure safety, the collaborative planning model, and the conflict prediction model are established. An improved grey wolf optimization algorithm is adopted to enhance the convergence speed and generalization performance. A case study at Ezhou Huahu Airport in China demonstrates the effectiveness of the methods. The results show that the model of collaborative planning can make the balance of path selection, Unmanned follow-me car’s working time, and departure sequence. The convergence speed of the improved algorithm has been increased by 18.75%. The inequity index of vehicle assignment is only 0.015731, and the spatiotemporal distribution of conflicts is influenced by the airport’s surface layout.

Aerodynamic/control integrated optimization method for unpowered high-speed vehicle configuration design

The unpowered high-speed vehicle experiences a significant coupling between the disciplines of aerodynamics and control due to its characteristics of high flight speed and extensive maneuverability within large airspace. The conventional aircraft conceptual design process follows a sequential design approach, and there is an artificial separation between the disciplines of aerodynamics and control, neglecting the coupling effects arising from their interaction. As a result, this design process often requires extensive iterations over long periods when applied to high-speed vehicles, and may not be able to effectively achieve the desired design objectives. To enhance the overall performance and design efficiency of high-speed vehicles, this study integrates the concept of Active Control Technology (ACT) from modern aircraft into the philosophy of aerodynamic/control integrated optimization. Two integrated optimization strategies, with differences in coupling granularity, have been developed. Subsequently, these strategies are put into action on a biconical vehicle that operates at Mach 5. The results reveal that the comprehensive performance of the synthesis optimal model derived from the aerodynamic/control integrated optimization strategy is improved by 31.76% and 28.29% respectively compared to the base model under high-speed conditions, demonstrating the feasibility and effectiveness of the method and optimization strategies employed. Moreover, in comparison to the single-stage strategy, the multi-stage strategy takes into deeper consideration the impact of control capacity. As a result, the control performance of the synthesis optimal model derived from the multi-stage strategy improves by 13.99%, whereas the single-stage strategy only achieves a 5.79% improvement. This method enables a fruitful interaction between aerodynamic configuration design and control system design, leading to enhanced overall performance and design efficiency. Furthermore, it improves the controllability of high-speed vehicles, mitigating the risk of mission failure resulting from an ineffective control system.

Material-structure-process-performance integrated optimization method of steel/aluminum self-piercing riveted joint

Material-structure-process-performance integrated optimization method of steel/aluminum self-piercing riveted joint

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.

Novel Modification of Integrated Optimization Method for Sensor's Communication in Wi-Fi Public Networks.

A novel modification of IP networks integrated optimization method for heterogeneous networks, for example, the seamless Wi-Fi network serving simultaneously mobile users and wireless sensors, has been developed in this article. The mutual influence of signal reception, frequency-territorial planning, and routing procedures in heterogeneous networks have been analyzed in the case of simultaneous data transmission by both mobile users and wireless sensors. New principles for the listed procedures interaction and the basic functions for their describing are formulated. A novel modification of the integrated optimization method and its algorithm have been developed. The developed method's effectiveness has been analyzed for the IEEE 802.11ax network segment. Its result showed that the network load was decreased by an average of 20%, the data rate over the network as a whole increased for users and sensors by an average of 25% and 40%, respectively, and the sensors' lifetime increased by an average of 20% compared to the novel modification of the Collective Dynamic Routing method.

Construction of vapor compression cycle with composite pressure-regulating components using GraPHsep method

Developing efficient vapor compression systems is a technical approach that effectively achieves low-carbon cooling and heating. An effective method for constructing vapor compression cycles is key to further improving the system’s energy efficiency. In our previous study, an integrated optimization method, called GraPHsep, was proposed for constructing the vapor compression cycle and heat exchange network. The method can synchronously optimize the structure and parameters of the cycle as well as the layout of the heat exchanger network. Composite pressure-regulating components that perform multiple adiabatic pressure-regulating processes, such as vapor-injected compressors, ejectors, and supersonic two-phase expanders, have the potential to improve the energy efficiency of the vapor compression cycle. In this study, the adaptability of the GraPHsep method is expanded to construct vapor compression cycles with composite pressure-regulating components. The general method consists of two steps: (1) solving the cycle topologies composed of pressure-regulating components using graph theory and (2) optimizing the heat exchanger network integrated with refrigerant and thermal fluids based on pinch analysis. The method is applied to construct a zeotropic vapor compression cycle with an ejector used for a high-temperature heat pump. The constructed cycle increases the coefficient of performance by 10.1% and 34.6%, respectively, compared to a single-stage compression cycle with a recuperator and that with an ejector using pure refrigerant.

Integrated optimization method for transonic turbine based on direct free-form deformation and prescreening differential evolutionary optimizer

With the increased load of turbine stages, shock waves and secondary flow influence the performance of turbines more and more significantly. Aiming at improving the aerodynamic efficiency of transonic turbines, an integrated aerodynamic optimization platform for turbines has been established. The aerodynamic shape of the flow path and blades is simultaneously parametrized by a directly manipulated free-form deformation (DFFD) method, and the optimization is conducted via a data-driven differential evolutionary algorithm. Furthermore, a prescreening strategy is introduced to enhance the convergence speed of the optimizer. The TTM-stage is selected as the research object. The result shows that the turbine isentropic efficiency at the design point increases from 90.45 % to 91.94 % after optimization. The main reasons for aerodynamic performance enhancement are the weakening of shock waves and the reduction of separation regions. As a comparison, another two optimization cases are carried out. One optimizes the blades individually with the same methods and configurations, and the other optimizes the blades and flow path simultaneously without the prescreening strategy. The optimized isentropic efficiency in the latter two cases is 91.31 % and 90.92 %, whichis 0.63 % and 1.02 % lower than the first optimization result, respectively. This shows the effectiveness of the proposed optimization method for turbine performance improvement.

Collaborative Planning in a Two-Stage Supply Chain: Transparent Coordination, Distributed Response, Integrated Optimization

In the competitive global market, large manufacturing enterprises are striving to achieve a competitive edge through constructing a transparent collaborative supply chain. One of the important and effective ways is to cooperate with distributed response from suppliers in the operation of the supply chain, such as production planning. This article proposes a new collaborative planning mode in a two-stage supply chain considering the supplier part production and product assembly simultaneously. In this context, a mathematical model is formulated and the integrated optimization method is proposed. To reduce the complexity of the problem, the part production decisions of suppliers are first addressed by some lemmas. Then, the transformation process of the mathematical model is described as the decision variables gradually decrease. On this basis, we further propose a LIMA-GVNDS (less is more approach-general variable neighborhood decomposition search) algorithm to solve the integrated supply chain planning problem for the assembly manufacturing enterprise. The objective is to minimize the total supply chain costs. A new neighborhood structure is designed according to the problem characteristics and a series of variable neighborhood descent (VND) variants are proposed. Some computational experiments are carried out, and the results show that the proposed LIMA-GVNDS algorithm has obtained an average 3.69%, 13.07%, 2.47%, and 2.99% improvement of objective value against the other four meta-heuristic algorithms. The model transformation and the integrated optimization method proposed in this article can provide a reference for large manufacturing enterprises to formulate collaborative planning in a transparent supply chain network.

Output and mechanical performance of thermoelectric generator under transient heat loads

Geometry optimization and pulsed inputs have been introduced as some of the effective methods to enhance the performance of thermoelectric devices. In this work, the geometric structure of a Bi2Te3-based thermoelectric module is optimized systematically using two-step integrated optimization method in steady state, and then the effect of transient heat flux on the output and mechanical performance is investigated deeply. The simulative results show that the optimization directions for mechanical and output performance are usually opposite. The optimal Ap/An, H/Anp and N of the Bi2Te3-based module are 1.6, 1.5 and 8, respectively. Moreover, the thicker He and thinner Hcs favor the improvement of output performance, however, considering the mechanical property, the optimal He and Hcs are regarded as 0.3 mm. In addition, using pulsed heat flux can improve the output performance and obtain a smaller average stress in a long period. The suitable duty cycle, smaller amplitude ratio and rectangular heat flux are beneficial for power enhancement and stress optimization. However, pulsed input brings frequent and repeated temperature changes, which is a challenge to the lifespan of thermoelectric modules. These simulated results will benefit the enhancement of output and mechanical performance of thermoelectric devices in the practical operation process.

Optimized Mask-RCNN model for particle chain segmentation based on improved online ferrograph sensor

AbstractFerrograph-based wear debris analysis (WDA) provides significant information for wear fault analysis of mechanical equipment. After decades of offline application, this conventional technology is being driven by the online ferrograph sensor for real-time wear state monitoring. However, online ferrography has been greatly limited by the low imaging quality and segmentation accuracy of particle chains when analyzing degraded lubricant oils in practical applications. To address this issue, an integrated optimization method is developed that focuses on two aspects: the structural re-design of the online ferrograph sensor and the intelligent segmentation of particle chains. For enhancing the imaging quality of wear particles, the magnetic pole of the online ferrograph sensor is optimized to enable the imaging system directly observe wear particles without penetrating oils. Furthermore, a light source simulation model is established based on the light intensity distribution theory, and the LED installation parameters are determined for particle illumination uniformity in the online ferrograph sensor. On this basis, a Mask-RCNN-based segmentation model of particle chains is constructed by specifically establishing the region of interest (ROI) generation layer and the ROI align layer for the irregular particle morphology. With these measures, a new online ferrograph sensor is designed to enhance the image acquisition and information extraction of wear particles. For verification, the developed sensor is tested to collect particle images from different degraded oils, and the images are further handled with the Mask-RCNN-based model for particle feature extraction. Experimental results reveal that the optimized online ferrography can capture clear particle images even in highly-degraded lubricant oils, and the illumination uniformity reaches 90% in its imaging field. Most importantly, the statistical accuracy of wear particles has been improved from 67.2% to 94.1%.

Optimizing the Mechanical Performance of Green Composite Materials Using Muti-Integrated Optimization Solvers

Natural fiber composites are potential alternatives for synthetic materials due to environmental issues. The overall performance of the fiber composites depends on the reinforcement conditions. Thus, this work aimed to optimize the reinforcement conditions of the natural fiber composites to improve their mechanical performance via applying an integrated scheme of Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and differential evolution (DE) methods considering various reinforcement conditions including fiber length, fiber loading, and treatment time for optimal characteristics of the composite mechanical performance. The B-Spline approximation function was adopted to predict the experimental performance of green composites. The B-Spline approximation function demonstrated incomparable accuracy compared to linear or quadratic regressions. The function is then optimized using an integrated optimization method. Results have demonstrated that optimal reinforcement conditions for the maximized desired mechanical performance of the composite were achieved with high accuracy. The robustness of the proposed approach was approved using various surface plots of the considered input-output parameter relations. Pareto front or the non-dominated solutions of the desired output mechanical properties were also obtained to demonstrate the interaction between the desired properties to facilitate finding the optimal reinforcement conditions of the composite materials.

An Integrated Optimization Method of High-Speed Railway Rescheduling Problem at the Network Level

For high-speed railway operations at the network level, unforeseen events that lead to operation interruptions are inevitable, which should be handled within a short period of time to reduce the influence of the events as much as possible. This paper introduces an integrated optimization method to deal with rescheduling problems at the railway network level under emergencies, rescheduling the train timetable, and utilizing the train sets. train set A three-objective optimization model is proposed with the aim of minimizing additional operation costs, total delay, and the number of transfer passengers. Then, an algorithm based on NSGA-III is proposed to solve the model. Computational experiments on real data are conducted to show the adaptability of the model and algorithm. The average optimization rate of the three objectives is 12.12%, 14.12%, and 10.57%, indicating the effectiveness of the method. Moreover, more experiments on a railway network in China are being conducted to analyze which section and which time have the greatest impact on the railway network when emergencies occur. According to the experiment, the bottleneck section is section 15, and the bottleneck time is 11:00 am. In addition, the importance of all the depots is discussed, and depot II is selected as the most important depot.

A study on optimal rotor speed control method for helicopter power system considering the influence of infrared suppressors

Abstract In order to realize the most economical operation of helicopter power system, the optimal rotor speed control method is proposed and designed. Firstly, the simplified performance calculation model of helicopter required power and turboshaft engine are established, which constitute the performance calculation model of helicopter power system together. Then, according to the above model, through applying blade loading constraint and selecting the minimum engine fuel flow as the optimization objective, an integrated optimization method of optimal rotor speed based on golden section method is proposed, which reveals the variation law of optimal rotor speed under different operation conditions. Finally, the numerical simulation and hardware-in-loop (HIL) simulation are conducted separately to validate the optimal rotor speed control method. The results indicate that compared with constant rotor speed operation, optimal rotor speed control method can decrease engine fuel flow by more than 21 %, which proves the significant effectiveness and satisfactory feasibility. The optimal rotor speed control method ensures that the helicopter power system operates at the optimal rotor speed, and is beneficial to reach the most economical cruise target.

Rotational Speed Optimization of Coaxial Compound Helicopter and Turboshaft Engine System

In order to enhance the overall performance and operational efficiency of the integrated coaxial compound helicopter and turboshaft engine system, a preliminary study on the optimal rotational speeds was implemented based on multidisciplinary integration. First, the flight mechanics model of coaxial compound helicopter and the aerothermodynamic model of turboshaft engine were developed. Then, the minimum fuel flow of the turboshaft engine was selected as the optimization objective, and an integrated optimization method of optimal rotational speeds based on the golden section method was proposed according to fixed ratio transmission (FRT) and continuously variable transmission (CVT). Finally, the effect of the optimal rotational speeds on the overall performance of the integrated system was investigated, and the variation laws of the optimal rotational speeds under different operating modes were revealed. The results indicated that the optimal rotational speeds can decrease engine fuel flow by a maximum of more than 10%, which is conducive to promoting operational economy and overall performance. Moreover, the flight conditions, helicopter weight, and engine degradation significantly affect the optimal main rotor speed.

Retracted: Integrated Optimization Method of IPPS under TOU and Tiered Electricity Price

Retracted: Integrated Optimization Method of IPPS under TOU and Tiered Electricity Price

Structural Optimization Design of a Six-Degrees-of-Freedom Serial Robot with Integrated Topology and Dimensional Parameters.

In the structural design of serial robots, topology and dimensional parameters design are independent, making it challenging to achieve synchronous optimization design between the two. To address this issue, a topology-and-dimension-parameter integrated optimization method (TPOM) is proposed by setting critical variables to connect topology layout and dimensional features. Firstly, the topology layout is extracted by the edge detection technique. Structural manufacturability reconstruction is conducted by measuring the dimensions of the layout through a program. Additionally, for the reconstructed structural layout, critical variables are set using three-dimensional software (SOLIDWORKS2021). The experiments primarily involve critical variables, quality, and deformation as variables. Then, the response surface methodology is selected to construct the stiffness-mass metamodel, and based on this, the structural deformation is analyzed. Lastly, the multi-objective genetic algorithm (MOGA) is employed to optimize the critical variables, and an optimized structure is established for validation. The results indicate that the proposed method (TPOM) reduces the mass of the structure by 15% while maintaining its stiffness. In addition, the deformation of the whole structure is less than 0.352 mm, which meets the requirements of industrial applications. Through quantitative analysis of the experimental results, the feasibility and superiority of the proposed method have been demonstrated.

Robust optimization design of permanent magnet synchronous motors for a solar airplane based on a lightweight structure

Due to the characteristics of high-altitude solar airplanes, the permanent magnet synchronous motors (PMSMs) require not only high torque density, but also high reliability. The balance of quality, reliability, and performance in a design proposal is particularly important. In this paper, a robust mechanical–electrical integrated optimization method for PMSMs based on 6σ theory is proposed. According to the fatigue characteristics of materials under the propeller working conditions and the uncertainty factors caused by the actual machining dimension tolerance, the electromagnetic dimension, mechanical dimension and reliability of the PMSM are connected. And a robust optimization model for the reliability and performance of the PMSMs is established. The Monte Carlo method and finite element simulation method are used to verify the correctness of the established optimization model. According to the comparison between the deterministic optimization method and the proposed optimization model, this method can significantly improve the output performance and reliability of the PMSMs.

Integrated speed modeling and traffic management to precisely model the effect and dynamics of temporary speed restrictions to high-speed railway traffic

This paper investigates the integrated optimization problem of train rescheduling and train control for high-speed railway lines, where perturbations occur and cause Temporary Speed Restriction (TSR) to trains.We consider microscopic details (i.e., block sections) for ensuring the feasibility of the solution from a train signaling point of view, and an even higher level of detail, to accurately represent the train speed profiles. Running time and headway time are variable, at the same time depending from, and affecting, traffic. We optimize train speed profiles by considering vehicle and infrastructure constraints (e.g., traction, slopes). The model naturally considers the transition along the normal, disturbed and the recovery operation periods.A mixed-integer nonlinear programming (MINLP) model is first developed to simultaneously optimize train orders, routes, and departure and arrival times, as well as train speed profiles, aiming at reducing total train deviation time. The MINLP model is difficult to solve; thus we further reformulate it into a mixed-integer linear programming (MILP) model by means of piecewise linear approximation. A two-step approach is designed to speed up the solving procedure of the MILP model: first estimate the upper/lower bounds of train speeds and then solve the MILP model based on the estimated bounds of train speeds.Three instances (i.e., a small-scale line, a medium-scale line, and a large-scale network) are used to highlight the performance of the approach, verify the benefit of the integration, and its dependence on the parameters used. According to the experimental results, our integrated optimization method leads to an average improvement of 3%-36% in solution quality, compared with the integrated approach without train rerouting measure. Moreover, the integrated optimization method outperforms the sequential approach, achieving 6%-9% improvement in solution quality.

Carbon awareness oriented data center location and configuration: An integrated optimization method

Serving as the information backbone of an increasingly digitalized world and energy-intensive, the data center industry has to seek low-carbon developing way to properly meet the rapidly rising service demand. This paper designs 45 kinds of configuration choices for each decision units by integrating three data centers, three cooling technologies, as well as five energy supply mixes. The model is formulated as a bi objective optimization problem for the decision maker to decide newly established data location and configuration with objective of reducing data center expansion costs and well as minimizing power utilization associated with carbon emissions from a country level. The pilot study in China under the scenarios of different carbon emissions limitation and propagation delay that China can reduce at least 25% of newly established data center related emissions from 2020 to 2023. It further shows that future expansion can be focus on Inner Mongolia and Ningxia for the north, Hubei for the middle, and Yunnan and Guangxi for the south. This paper provides a fresh perspective and makes a valuable attempt at the low-carbon development of data centers.

Integrated optimization of 3D structural topology and 2D Halbach parameters for maglev planar motor

With the increasing demand of mechanical stiffness performance and electromagnetic performance of maglev planar motor, especially the moving-magnet motor with Halbach array, an integrated optimization with 3D structural optimization and magnet parameter optimization is proposed and executed in this paper. The macroscopic electromagnetic performances consisting of thrust-mass ratio and power dissipation are taken as the optimization objective, which improves the performance more directly and comprehensively than traditional optimization methods. A streamlined dual-loop optimization framework is constructed and proved to be able to reduce the computational consumption by tens of times compared to the initial conventional framework, authentically guaranteeing a smooth integrated optimization. At the outer loop of the optimization framework, this paper proposes an oriented strategy for the individuals of Genetic Algorithm (GA) to quickly meet the strict constraints. The integrated optimization method solves the parameter coupling problem between electromagnet and stiffness. Compared to existing optimized structure, the resulting structure improves the thrust-mass ratio by 7.7%, power dissipation by 12.8% and natural frequency by 16.6%, respectively. Mechatronic experimental results in maglev planar motor systems show that the proposed integrated optimization can significantly improve the control bandwidth and reduce the current consumption, laying the foundation for better performance in practical applications.