Multi-objective Design Research Articles

Numerical optimization of hydrothermal and thermodynamical performance for metal foam multi-jet impingement cooling

A novel porous multi-jet impingement cooling technology since the current conventional cooling technologies are insufficient for thermal engineering devices. This innovative numerical study explores the effect of various metal foam sizes (h/H), jet numbers (n) and wavy jet target plates (C) on the hydrothermal performance and thermodynamical performance at varying Reynolds numbers (Re). The novel results of multi-objective optimum design are a unique aspect of this work by combining the results of RSM with CFD, where low porosity (ε = 0.1), low applied heat flux (Q = 100 × 103 W/m2) and low location jet ratio (LRVin = 0.26) were achieved the aim of the optimum hydrothermal performance (NumP/NumS = 25-30 times and Tb Reduction% = 83.5%-89.5%) and the thermodynamical performance (EG Reduction% = 93%-96% and CoPCarnot = 2.3-6.5 times). Thus, a valuable procedure for the optimal design of the hydrothermal and thermodynamical performance for porous multi-jet impingement cooling is proposed.

Multi-objective design optimization of hermetically sealed core-type distribution transformer considering current harmonics of power grid using NSGA III

In this paper, the design optimization of hermetically sealed oil-immersed distribution transformers is studied. For the first time in the design of this type of transformer, harmonics of the power grid affecting the transformer design, along with all design variables, are included, and their boundaries suitable for the production line are determined. The design probability (search space) for a total of 21 design variables is 1.32×1025. The Brute Force method is not a time-efficient approach to finding the optimal result in this search space. Due to the mixed integer nonlinear programming (MINLP) nature of the transformer design problem, heuristic optimization algorithms are selected because of their shorter solution time and near-optimal results. Considering the principle of the “No Free Lunch” theorem, extensive evaluations were conducted on various heuristic algorithms to identify the most effective ones for addressing the transformer design problem. The most successful algorithms for solving the problem were identified after thorough evaluation, considering three distinct scenarios within a multi-objective optimization framework. These algorithms are namely MOEA/D, MOGWO, MOWOA, and NSGA III. In this study, an optimization tool is developed for the design optimization of hermetically sealed distribution transformers, covering a range from 50 kVA to 3150 kVA and encompassing all design variables. The developed optimization program is fully compatible with the production line and can be easily adjusted to meet all national and international standards, such as IEC and IEEE, as well as customer specifications.

Multi-objective optimization design of a solar-powered integrated multi-generation system based on combined SCO2 Brayton cycle and ORC using machine learning approach

In order to reduce the use of fossil fuels and meet the needs of different energy products, this paper proposes an integrated multi-generation system that can produce power, cooling, and freshwater. A mathematical model is established to analyze the system performance from the perspectives of energy, exergy and economics. Different machine learning algorithms are used to develop prediction models of the system performance. By comparing the predictive performance of different machine learning models, the optimal model is obtained to replace the thermodynamic model of the proposed system to accelerate the optimization process. The results show that the backpropagation neural network (BPNN) model demonstrates the highest predictive accuracy and the lowest relative error fluctuation. When comparing the multi-objective optimization results of the BPNN model with those of the thermodynamic model, it can be observed that the results achieved through both models are very close. Furthermore, the optimization time using the BPNN model is significantly shorter than the thermodynamic model. Finally, under the conditions of solar radiation intensity of 950 W/m2, heliostat field area of 1000 m2, and heat source temperature of 838.15 K, the total product output, thermal efficiency, exergy efficiency, and levelized cost of energy at the final optimal point obtained through BPNN model-based optimization are 2.486 MW, 26.16 %, 23.64 %, and 0.105 $/kWh, respectively. The net power output, cooling capacity, and freshwater flow rate are 2.006 MW, 131.06 kW, and 8.27 m3/h, respectively.

Bi-level game theoretic approach for robust design: A case study of path-generating four-bar

This study addresses the bi-level multi-objective optimization problems (MOP) that raise in robust design and optimization of engineering systems through establishing a state-of-the-art game theoretic scenario. A novel leader-follower decentralized decision-making scenario is proposed, leveraging the synergy of game theory, Robust Design Optimization (RDO), Monte Carlo Simulation (MCS), and Artificial Intelligence (AI). The proposed algorithm can be employed for optimum robust Pareto design of a wide range of dynamical systems. In order to achieve a robust design, both the mean and variance of each objective function are considered as players in a multi-agent game setting. In this approach, both Stackelberg and cooperative games are utilized to model the behaviors of the players. Genetic Programming (GP) meta-models are employed to capture the Stackelberg protocol between two levels specifically for constructing the follower's rational reaction set (RRS). Additionally, the Nash bargaining function is -utilize to model the cooperative behaviors among players in each level. The proposed approach is applied and demonstrated through a case study involving multi-objective robust design of planar four-bar linkages. In this manner, four objective functions are assigned to four players within the system. Each player is responsible for optimizing a specific objective criterion, namely the mean of tracking error (TE), variance of tracking error, mean of transmission angle and variance of transmission angle (TA) of the linkage. As a result, the four-objective optimization problem of mechanism is transformed into a single-objective robust synthesis problem. The comparisons of the results show a significant enhancement in the robust behavior of the linkage, while ensuring that deterministic criteria such as quality of motion and precision are preserved.

Multi-objective early-stage design optimization of multifamily residential projects

The aim of this study is to find a building form and plan layout that can be used in the early stages of architectural design, where criteria such as daylight, view, sun-hour, sales area, and cost are optimized according to the different expectations of different housing type users. This study proposes a multi-objective early-stage design optimization for a real estate development project based on the NSGA2 genetic algorithm, considering weighted user preferences for different housing types. The framework is implemented using the platforms Rhino and Grasshopper; Wallacei is used for NSGA2, and Viktor.ai is used to deploy the app. Tested on six sample plots, the model was able to find architecturally optimized results that respond to different user expectations. While the model successfully demonstrated responsiveness to parameters, its focus on Pareto-optimal solutions limited the diversity of unit mixes generated. The model has been tested by professionals on a sample plot and is found to be important for architects and investors to generate ideas at an early stage of architectural design.

Multi-objective optimization design of high-pressure plate-fin ammonia boiler based on MO-SHERPA algorithm

With the rapid development of electrification, the high-pressure plate-fin ammonia boiler has been widely used as an effective thermal management solution, which has stimulated the demand for its structure design and optimization. The aim of this study was to establish a multi-objective optimization design method for high-pressure plate-fin ammonia boiler based on the hybrid and adaptive search strategy of MO-SHERPA and CFD simulation. Based on this method, a set of pareto-optimal solutions were obtained, and then the optimal configuration with lightest weight and high heat transfer performance was determined by calculating the deviation for weighted sum of comprehensive performance factor, entropy generation rate and structure weight. The effects of fin structural parameters of ammonia boiler were further examined on its performance. The results showed that increasing the spacing and height of offset strip fin and reducing its thickness could effectively improve the heat transfer performance. This study provides a theoretical method and some guidelines for the structural design of ammonia boiler with high operating pressure.

Multi-Objective Optimization Design of 40 MW High-Temperature Superconducting Motor

Abstract To reduce the length of the superconducting wire of the superconducting motor, the no-load back EMF E and the magnetic field component perpendicular to the wire B⊥ were selected as the optimization goals. Therefore, a multi-objective optimization method based on the Response Surface Method (RSM) and Genetic Algorithm (GA) was proposed by taking a high-temperature superconducting motor (HTSM) with a rated power of 40 MW as the research object. Firstly, the overall structure, mathematical model, and output characteristics of HTSM were introduced. Secondly, the sample points required for constructing the RSM equation were obtained by Finite Element Analysis (FEA), and the multi-objective optimization of the RSM equations was performed by applying GA to obtain the Pareto optimal frontier. Finally, the calculated optimal design was simulated and verified. Compared with the initial design, the E of the optimized HTSM was increased by 4.47%, and the B⊥ was reduced by 4.83%. Based on the motor parameters obtained by multi-objective optimization, the final optimized design of HTSM was obtained. The length of the superconducting wire used in the motor has been reduced by 10.2%, which greatly reduces the cost.

Prescreening surrogate-model-assisted multi-objective aerodynamic optimization design of highly loaded axial compressor in heavy-duty gas turbine

To address the challenges of numerous design variables and high evaluation costs in the aerodynamic optimization of advanced highly-loaded axial compressors, this paper presents an axial compressor aerodynamic optimization platform based on directly manipulated free-form deformation (DFFD) parameterization method, a data-driven multi-objective differential evolution optimizer, and computational fluid dynamics. The DFFD method allows for direct geometric manipulation of the target compressor, enabling efficient parameterization of the blades and flow path and reducing the number of design variables. A prescreening surrogate-model-assisted multi-objective differential evolution (Pre-MODE) optimizer is developed to obtain competitive solutions within a limited number of iterations. The surrogate model in Pre-MODE conducts real evaluations only on selected individuals in each generation, thereby reduce evaluation costs while ensuring prediction accuracy. Additionally, a parallel task management system is integrated to improve optimization efficiency. To verify the effectiveness of the optimization platform, a 1.5-stage highly-loaded compressor is aerodynamically optimized. After optimization, the adiabatic efficiency at the design point and the surge margin are increased by 2.42% and 9.04%, respectively. These results confirm the effectiveness and speed of the aerodynamic optimization platform, promoting the establishment of an integrated optimization design system for axial compressors.

Analysis of Amplification Effect and Optimal Control of the Toggle-Style Negative Stiffness Viscous Damper

This paper proposes a new toggle-style negative stiffness viscous damper (TNVD), and evaluates the performance of the TNVD with the displacement amplification factor (fd) and the energy dissipation factor (fE). Firstly, the composition and characteristics of the TNVD are introduced. Subsequently, the displacement amplification factor is introduced to evaluate the displacement amplification ability of the TNVD, and it is decomposed into a geometric amplification factor and an effective displacement coefficient. Then, based on the geometric amplification factor and effective displacement coefficient, the correlation between the TNVD’s displacement amplification ability and inter-story deformation is studied, and an improved TNVD is proposed. By the comparison of the finite element calculation results, it is found that the improved TNVD can utilize the assumption of small structural deformation. After that, the impacts of plentiful aspects, such as the length of the lower connecting rod, the horizontal inclination angle of the lower connecting rod, the inter-story deformation limit, the cross-sectional area of the connecting rod, the damping coefficient, and the negative stiffness on the fd and fE of the improved TNVD, are expounded. The research results show that when the length of the TNVD’s lower connecting rod remains unchanged, the fd and fE present a trend of increasing first and then decreasing with the increase in the horizontal inclination angle of the lower connecting rod. When the inter-story deformation is fixed, there exists an optimal lower connecting rod’s length that satisfies a specific relationship to achieve the optimal geometric amplification factor of the TNVD. By adjusting the damping parameters of the TNVD, we can obtain a better effective displacement coefficient greater than 0.95 in the proposed target region. Meanwhile, the fd and fE increase with the decrease in the negative stiffness. An optimization strategy for the improved TNVD has been proposed to ensure that the TNVD has the characteristics of operational safety, ideal displacement amplification capability, and energy dissipation capability. Furthermore, a multi-objective control design method with an additional improved TNVD structure is proposed. The vibration reduction effect of the structure with the improved TNVD and the effectiveness of the optimization strategy are verified through examples.

Additional Acceleration of Antenna Optimal Characterizationwith Modeling Support

Optimal characterization is assumed to provide the best solution for the designed cost function among the possible solutions within the specified range. These processes can take a long time depending on the applications and computer hardware used. Here, the optimization process is supported by ANN modeling in order to shorten the current optimization processes as much as possible. For this purpose, the selection of design parameters of the bowtie patch antenna (BPA) is presented as a multi-dimensional, multi-objective modeling-supported design optimization problem. The operating frequency of the proposed antenna is 28 GHz, which is the standard for millimeter wave band and 5G technologies. To overcome this challenging design optimization, a new, fast and powerful optimization algorithm was used by modifying the non-dominant sorting genetic algorithm (NSGA)-III, and the optimal characterization of the microwave antenna design was achieved. Although the proposed method gives the same results compared to the existing process, it takes much less time. Therefore, it is possible to shorten the process and reduce costs without the need for extra applications or hardware. As a whole, the proposed design optimization process is an efficient, fast and reliable solution for all design problems.

NSGAN: a non-dominant sorting optimisation-based generative adversarial design framework for alloy discovery

The design and discovery of new materials is fundamental to advancing scientific and technological innovation. The recent emergence of the materials genome concept holds great promise in revolutionising materials science by enabling the systematic utilisation of data for efficient prediction and optimisation of ‘superior’ materials. However, the materials genome approach can be stymied by the vast complexity of design spaces, which often demand substantial computational resources and sophisticated data processing capabilities. To address these challenges, this work introduces a generative design framework called the non-dominant sorting optimisation-based generative adversarial networks (NSGAN). Capitalising on the synergies of genetic algorithms (GA) and generative adversarial networks (GANs), NSGAN provides a robust and efficient approach for tackling high-dimensional multi-objective optimisation design problems. To validate the efficacy of the proposed framework, we applied the model to a comprehensive dataset of aluminium alloys. Additionally, an online tool was created as a supplementary resource, offering a brief introduction to this innovative method for the wider scientific community. This study explores the potential of a predictive and data-driven approach in material design, indicating a promising pathway for widespread applications in the field of materials science.

Design and Analysis of an Interior Permanent Magnet Synchronous Motor for a Traction Drive Using Multiobjective Optimization

With the development of new energy industries, the demand for the driving range and power quality of electric vehicle (EV) drive systems is growing rapidly. The drive motor is faced with the challenge of continuously improving power density and performance. This paper proposes a multiobjective optimization method for an interior permanent magnet synchronous motor for a traction drive (IPMSMTD). Based on the flat wire winding technology, the multiobjective optimization design of the IPMSMTD is carried out to improve the motor power density and high-efficiency range, reduce the torque ripple, and suppress the electromagnetic vibration and noise. The structure and size equation of the IPMSMTD are described. The mathematical model considering iron losses is established, and the optimization objectives are determined. Based on the genetic algorithm, a multiobjective optimization mechanism of the magnetic pole structure is established. The operation performance of the motor is analyzed by the finite element simulation and efficiency map. In order to ensure the comprehensive operation index of the IPMSMTD, the vibration noise and modal analysis are carried out, which verifies the rationality of the designed motor and the optimization method.

A multi-objective optimization design method for underwater composite sandwich structures with high acoustic transmission and low vibration performance

ABSTRACT To address the issue of reduced structural integrity and optimality resulting from the conventional serial design of sonar dome structures, this paper establishes a multi-objective optimal design model for composite sandwich structures, utilising the Non-dominated Sorting Genetic Algorithm-II (NSGA-II). The model's stability and reliability are subsequently validated. Upon this model, a structural optimization design technique is postulated for composite sandwich structures within the realm of Fluid-Structure Interaction (FSI) multi-physics coupling. This approach aims to optimize the lightweight, high-rigidity composite sandwich structure, enhancing its acoustic transmission and minimizing vibration performance under turbulent pressure pulsations. The suggested method notably enhances the efficiency of structural design while ensuring precision. The proposed method effectively addresses the deficiency of serially designed sonar domes by neglecting the synergistic effects among multiple disciplines. In summary, this approach offers technical support and new research perspectives for the design of sonar domes under the coupling of multiple physical fields.

Multi-objective topology optimization design of thermal-mechanical coupling structure based on FPTO method

Multi-objective topology optimization design of thermal-mechanical coupling structure based on FPTO method

The role of storage in energy security performance based on diversification and concentration for distributed energy systems

This work addresses the use of diversification and deconcentration strategies of primary sources of energy supply for the multi-objective design of distributed generation systems based on renewable sources. The Shannon–Wiener and Herfindahl–Hirschman indices are proposed as optimal design objectives. The utopia-tracking method is used to identify trade-offs between the total annual cost of the system and the efficiency of the storage system. The storage system is included in the assessment of diversification and deconcentration due to its functions as a backup unit in the event of adverse system operating conditions. Cases of interconnected and off-grid systems are addressed. The results show that, by including the storage system, an over-diversification of supply sources is generated and that, in the absence of various sources, as occurs in off-grid systems, storage generates sufficient flexibility to reach a trade-off among economic performance, diversification of supply sources and energy efficiency. The robust nonlinear programming model is fed with real data from a residential building.

Optimizing multi-objective design, planning, and operation for sustainable energy sharing districts considering electrochemical battery longevity

The urgent need to address the global warming crisis has led to a paradigm shift in energy production from traditional fossil fuels to renewable sources such as geothermal, solar, and wind. This transition necessitates efficient energy management strategies to enhance renewable energy utilization, reduce microgrid dependency, and establish a more stable power grid. This study explores the novel integration of interactive energy sharing networks utilizing electrochemical battery storage, emphasizing detailed modeling of battery degradation, smart energy management, and multi-criteria decision-making. This research involves an innovative method combining Monte Carlo Tree Search technique and a multi-criteria approach for energy management in district communities. The research delves into the complexities of integrated multi-energy systems, considering structural designs, advanced modeling methods, and multi-criteria predictions. It also employs powerful machine learning methods to predict battery depreciation, demonstrating their superiority over standard semi-empirical models. The study also proposes deterministic and stochastic methods for intelligent energy management, considering unpredictable factors like weather and energy demand profiles. The research incorporates a comprehensive examination of interactive energy sharing networks, encompassing renewable-to-vehicle and vehicle-to-building interactions. Through an in-depth analysis this research highlights the advantages of multi-agent systems, time-of-use energy shifting, demand profile flattening, and micro-grid resilience.

Multi-objective optimisation research of ship form in waves based on Kernel principal component analysis

ABSTRACT In the process of multi-objective optimisation design of ship forms, subjective weighting methods rely on experts’ experience and have a certain impact on the scientificity of results. The objective weighting method can effectively solve this drawback and reduce the interference of human factors. Therefore, this paper proposes a multi-objective optimisation design programme for ship forms based on KPCA method, and constructs a multi-objective optimisation design framework. In this paper, the KPCA method is used to calculate the contribution rate of each indicator for objective weighting, give the comprehensive evaluation score of the optimal solution set and make decisions, solve the technical drawbacks of subjective weighting, and obtain a satisfactory optimal hull form. The results show that the automatic multi-objective optimisation design method proposed in this paper has certain engineering application value, and can provide theoretical basis and technical support for green design and manufacturing of ships.

Design and Experiment of an Ankle Rehabilitation Robot After Fracture Surgery

Abstract In order to address the problem of functional rehabilitation after ankle fracture surgery, this paper presented a novel ankle fracture rehabilitation robot. The robot adopted R-3RRS-P hybrid structure, which was simple in structure and had two working modes: rehabilitation training and motion axis switching. Compared with the existing ankle rehabilitation robot, the proposed robot could simulate more realistic kinematics of the ankle joint complex. Additionally, different body types of patients could be adapted. The kinematic and static models were established in detail using geometric method and screw theory. The coverage of the healthy ankle motion ability was formulated as an optimization problem to improve the robot's performance. Multi-objective optimal design was carried out to determine the dimensional parameters. The interference-free working space was calculated by numerical method. A prototype of the proposed robot was developed, and a series of experiments were performed to evaluate the function and feasibility of the proposed robot.

Multi-objective Optimization Design of Six-phase U-shaped Permanent Magnet Linear Vernier Motor

Multi-objective Optimization Design of Six-phase U-shaped Permanent Magnet Linear Vernier Motor

Heat dissipation analysis and multi-objective optimization of Raspberry Pi CPU by natural convection

With the high-speed and high-frequency operation of CPU chips, as well as miniaturization and dense assembly of integrated circuits, the heat generated by the Raspberry Pi CPU is constantly increasing. To address the urgent need for efficient Raspberry Pi CPU heat dissipation and ensure normal operation within a safe temperature range, this study first established a model to simulate the CPU's thermal physics parameters during operation using CFD simulation software. An experimental platform was then set up to validate the CFD simulation results. Based on the validated CFD model, initial heat dissipation design was carried out. On this basis, the heat dissipation system was optimized by altering the heat sink’s length, width, fin height, thickness, spacing, and the thermal grease’s thermal conductivity between the heat sink and CPU chip. A surrogate model was established using the response surface methodology, and the NSGA-II genetic algorithm was employed for multi-objective optimization of the heat dissipation system’s cooling performance. Compared to the initial solution, the chip temperature was reduced by 6.19 °C, but the heat sink mass was increased by 0.65 g. By conducting multi-objective optimization design and making reasonable choices for the parameters, a suitable and an optimum design, in terms of both economics and efficiency, is obtained. These results demonstrate that this research can provide theoretical guidance and technical support for practical production. Furthermore, the methods and findings from this study can be applied to the design of heat dissipation systems for similar applications.