Multi-objective Design Optimization Research Articles

Multi-objective optimization design of a sewage pump based on non-dominated sorting genetic algorithm III

Accumulation of sanitary refuse, such as flexible cloth-like structures or the so-called rags, inflows through sewage pumps are prone to tangling, ultimately leading to clogging and wear. To prevent this, the ability of sewage pumps to handle wet wipes, rags, and similar flexible materials is a key feature that must be considered as the pumps are designed. Therefore, this paper proposed a multi-objective optimization strategy based on the fluid–structure interaction simulation, Support Vector Regression (SVR), and non-dominated sorting genetic algorithm III (NSGA-III). First, the values of the optimization objectives were obtained by a Coupled Computational Fluid Dynamics-Discrete Element Method (CFD-DEM) approach. Then, SVR was utilized to establish an approximate model between the design variables and the optimization objectives. The NSGA-III was applied to search the Pareto front. Finally, the improved impeller model was selected by adopting technique for order preference by similarity to an ideal solution (TOPSIS) with entropy weight. The results show that the multi-objective optimization method is suitable for the optimization design of sewage pumps. Comparing the numerical calculations of the original pump and the optimized pump, the results show that the optimized head and efficiency increased by 9.7% and 7.13%, respectively. The optimized pump improves the passage rate of the rag and effectively improves the clogging behavior. The wear amount of the optimized pump is significantly reduced by 32.54%.

A case study of multi-objective design optimization of a healthy building in Shanghai, China

Energy efficient healthy buildings design is important in achieving carbon neutrality and occupants’ health, yet not sufficiently explored. This paper aims to optimize a healthy building in Shanghai, China, based on energy consumption, indoor air quality and visual comfort. A four-step optimization method was proposed. Firstly, Latin Hypercube-Soubert sampling method was used to generate 375 design samples and performance data through computer simulation and calculation. Secondly, five different machine learning approaches were applied for prediction model development. Thirdly, the best prediction models were coupled with eleven optimization algorithms to find Pareto solutions. Finally, optimization on different combinations of design parameters were conducted. It was found that the back propagation neural network and Pareto Envelope-based Selection Algorithm II were the best prediction models and optimization algorithm. The average reduction of building energy consumption, visual discomfort, and improper indoor air quality hours were found to be 25.73 %, 46.24 %, and 38.34 %, respectively. The recommended windows to wall ratios of the east, south, west and north walls, absorptance of solar radiation and filter types are in the range of 20%–35 %, 10%–45 %, 10%–30 %, 10%–40 %, 0.7–0.9, and S7-S9, respectively. This study contributes to enrich the case study of healthy buildings, provide a quick design optimization approach and analysis on the importance of each design parameter. Its originality lies in the optimization methodology, comparative analysis of prediction models, optimization algorithms, and combination effects of design parameters. The outcomes can guide the building designers in performing energy efficient design while maximizing indoor air quality and visual comfort.

Multi-Objective Robust Design Optimization for Crashworthiness Enhancement of Hybrid 2D Triaxially Braided Composite Tube Using Evolutionary Algorithms.

An innovative optimal design framework is developed aiming at enhancing the crashworthiness while ensuring the lightweight design of a hybrid two-dimensional triaxial braided composite (2DTBC) tube, drawing insights from the mesostructure of the composite material. To achieve these goals, we first compile the essential mechanical properties of the 2DTBC using a concentric cylinder model (CCM) and an analytical laminate model. Subsequently, a kriging surrogate model to elucidate the intricate relationship between design variables and macroscopic crashworthiness is developed and validated. Finally, employing multi-objective evolutionary optimization, we identify Pareto optimal solutions, highlighting that reducing the total fiber volume and increasing the glass fiber content in the total fiber volume are crucial for optimal crashworthiness and the lightweight design of the hybrid 2DTBC tube. By integrating advanced predictive modeling techniques with multi-objective evolutionary optimization, the proposed approach not only sheds light on the fundamental principles governing the crashworthiness of hybrid 2DTBC but also provides valuable insights for the design of robust and lightweight composite structures.

Multi-objective optimization of engineered cementitious composite based on machine learning and generative adversarial network

This study aims to establish a novel framework for mixture design optimization of engineered cementitious composite (ECC) by first collecting two original datasets of ECC's tensile stress and strain from the extensive and credible literature. The datasets comprise a wide range of variables including cementitious ingredients, 9 types of fiber and characteristics, admixtures, and experimental conditions. The data augmentation is then performed using a tuned constraints-modified Conditional Tabular Generative Adversarial Network (Tuned-CTGAN) to increase the model accuracy and generalizability. The fitness functions of tensile stress and strain are established based on four machine learning models with the hyperparameters tuned by the Hunger Games search (HGS) algorithm. After the data augmentation, the values of R2 in their test sets are increased from 0.874 to 0.925 and from 0.772 to 0.889, respectively. Subsequently, the third objective function (cost) is computed by polynomials and four classes of constraints (Min-max, volume, ratio, and fiber) are set up to define the variable's search space. A non-dominated sorting genetic algorithm based on reference-point strategy (NSGA-III) is introduced to optimize the mixture proportions of ECC by simultaneously optimizing tensile stress, tensile strain, and cost. This paper combines the results of data augmentation, model prediction, and multi-objective optimization for complex ECC design, which aims to provide a basis for practical application.

Multi-objective design optimization and physics-based sensitivity analysis of field emission electric propulsion for CubeSat platforms

Field-emission electric propulsion is an electrostatic space electric propulsion technology that offers various advantageous features including efficient design, high specific impulse, and versatile thrust capabilities ranging from micro-Newton to milli-Newton levels. These characteristics make this type of propulsion a promising technology for small satellite platforms, enabling precise attitude control, orbit maintenance, and de-orbiting through ionization and acceleration of a liquid metal propellant. The growing demand for small propulsion systems in CubeSat platforms has spurred significant progress in modeling and characterizing field emission electric propulsion thrusters to enhance their overall performance. However, little study has been conducted to investigate the effect of geometric configurations on electric fields or expelled ion trajectories for design optimization. In this study, multi-objective design optimization is performed by incorporating electrostatic simulation coupled with an analytical performance model into evolutionary algorithms based on prediction from surrogate modeling, aiming to optimize the thruster emission design to maximize thruster performance. Physical insights into the key design factors influencing the performance of field emission electric propulsion have been gained by probing into the interaction between ion particles and electric field behavior within the thruster. It has been found that the length of the emitter tip has a significant effect on plume divergence, i.e., a longer emitter tip under the influence of electric field at higher emitter current tends to result in lower initial acceleration of emitted ions and subsequently wider spread or divergence of the ion beam. A shorter emitter tip, on the other hand, generates a sharper E-field gradient, resulting in a more focused and narrower ion beam. Additionally, sensitivity analysis has identified the mass flow rate and potential distributions as the most influential design factors on performance due to the active roles they play in the performance generation process.

Multi-objective robust optimization design framework for low-pollution emission burners

The optimal design of low-pollution emission burners plays an important role in controlling pollutant emissions of industrial equipment, and is crucial for the sustainable development of the national economy and environmental protection. However, many uncertain factors challenge the optimal design of low-pollution emission burners. The Latin hypercube sampling (LHS) method was used to obtain sampling data representing the distribution of the uncertain variable. The training dataset was obtained using the turbulent combustion coupling model. A high-precision sparse polynomial chaos expansion (PCE) model was constructed by the degree-adaptive scheme and least angle regression (LAR) algorithm. Furthermore, the Legendre polynomial is introduced to establish a continuous robust optimization model. The model is carried out by the non-dominated sorting genetic algorithm II (NSGA-II). The results show that the excess air coefficient of 1.227 is optimal. Compared with the excess air coefficient of 1.20 under the discrete robust optimization, the optimal coefficient can further reduce pollutant emissions and bring strong robustness to the ethylene cracking furnace. It has also been proven that the continuous robust optimization scheme improves the optimization granularity. Compared with discrete robust optimization, this method reduces the number of samples by 66.7 %.

Comparative assessment and optimization among several plenum shapes and positions for the forced air-cooled battery thermal management system

Plenum shape and position play a significant impact on the heat dissipation performance of battery pack with air-cooled structure. However, the existed plenum shape and position are simple. In this work, four different plenum shapes (slanted, convex, concave and stepped) of the battery thermal management system (BTMS) are designed. Then the influences of plenum shapes and positions (at the inlet, outlet, and inlet & outlet) on the heat dissipation performance of the battery are investigated, respectively. Subsequently, a single factor analysis is performed to obtain the range of the design parameters (Hin, Hout, Rin and Rout) in Design 6. Finally, a multi-objective optimization of Design 6 is carried out to further improve the thermal performance of BTMS. Numerical results demonstrate that: 1) For the significant improvement of the cooling ability, convex plenum is at the inlet & outlet whereas other shape ones are all at the inlet. 2) No matter where it is at the inlet, outlet, or inlet & outlet, the convex plenum achieves the excellent cooling effect in Z- and U-type air-cooled structures. 3) Design 6 (convex plenum at the inlet & outlet) exhibits the best cooling ability among all plenum shapes and positions. 4) The design parameters of the optimized Design 6 are Hin = 17.9 mm, Hout = 19 mm, Rin = 2100 mm and Rout = 2000 mm, and the maximum temperature and temperature difference respectively achieve a 35.06 % (18.921 K) and 76.98 % (19.923 K) reduction under such optimized design parameter settings in comparison with those of the benchmark (Design 9).

Multi-objective optimization design of aerothermal performance for turbine blade squealer tip with inclined suction-side rim

To optimize the inclined rim design, multi-objective optimization was numerically conducted on the suction-side rim of the squealer tip in the GE-E3 rotor blade. The RANS equations, incorporating with the standard k-ω model, were numerically solved. Through the implementation of a Kriging surrogate model coupled with the NSGA-II evolutionary optimization technique, six geometric parameters on the suction-side rim went through iterative optimization. This process yielded three optimized squealer tip geometries on the Pareto front: Structure 1 with minimum total pressure loss, Structure 2 with optimal comprehensive aerothermal performance, and Structure 3 with minimum tip heat load. Comparative analysis revealed that with the inner and external wall inclined toward the cavity and passage respectively, the optimization results in a 3 %–11 % reduction in tip heat load, accompanied by an 8.5 %–10.2 % reduction in aerodynamic loss, facilitated by an attenuated upper passage vortex (UPV) and strengthened tip leakage vortex (TLV). The critical inclined region of the external rim wall, spanning 7 %–80 %, influences the exit total pressure loss distribution. The inclined inner wall upstream of 50 % Cax obstructs coolant jets from exiting the cavity, reducing the heat load on the cavity bottom. However, the inclined external wall from 66 % to 90 % Cax amplifies the impingement of the scraping vortex (SV), increasing local heat transfer. The thermal behavior of Structure 1 is sensitive to changes in tip clearance, whereas Structure 2 and 3 show more robust advantages. As the clearance increased, the aerodynamic losses associated with the TLV rise for all three optimized tips, but losses from the UPV remain similar. This study provides a novel approach for designing efficient squealer tip geometries incorporating an inclined rim.

Tradeoff analysis of the energy-harvesting vehicle suspension system employing inerter element

This paper explores the vibration isolation performance and vibration energy recovery performance of an energy-harvesting vehicle suspension system employing inerter element. The study takes into account the structural changes in the suspension caused by introducing the inerter as a new type of vibration isolation element. According to the parallel-series combination of an inerter and a damper, two different structures of energy-harvesting suspension dynamic models are constructed. The mechanism of the suspension parameter uptake on vehicle ride comfort and energy-harvesting characteristics is analyzed. A multi-objective optimal design method for the energy-harvesting vehicle suspension system employing inerter element is proposed, which considers both vehicle ride comfort and energy-harvesting characteristics. A trade-off was found between suspension isolation performance and energy-harvesting efficiency. The results indicate that various structures of energy-harvesting vehicle suspension systems employing inerter element exhibit different vibration isolation performances. Compared with the conventional energy-harvesting suspension, the series structure reduces body acceleration by 15.9 %, and the root mean square (RMS) of energy-harvesting power of the suspension is 22.2 W. The parallel structure reduces the RMS of body acceleration by 14.3 % and the RMS of energy-harvesting efficiency is 56.3 %, with the RMS energy-harvesting power of 84.9 W. The parallel structure demonstrates superior overall performance.

Multi-objective optimization for energy-efficient building design considering urban heat island effects

Building energy performance (BEP) associated with climate change and urban heat island effects (UHI) play an important role in urban sustainable development. To predict and optimize BEP under various socioeconomic scenarios, a new framework combining the physical simulation modeling integrated explainable machine learning and multi-objective optimization is proposed in this study. A Grasshopper-based simulation model incorporates BO-LGBM (Bayesian optimization-LightGBM) is developed to construct a solid prediction system, which tends to tune the hyperparameters accurately and explain more details with the aid of SHapley Additive explanation (SHAP). Two major aspects, including the building energy use intensity and indoor thermal comfort, are modeled by considering the different Shared Socioeconomic Pathways (SSPs) climate change scenarios in the near and far future. A multi-objective optimization method is employed to find an optimal solution for energy-efficient building design under constraints or uncertainties. Key findings include a 54% improvement in the Pareto front for building energy optimization and a significant impact of SSP585 scenarios on future energy consumption. The main novelty lies in the incorporation of machine learning into a physical model to achieve energy-efficient building design in urban contexts by considering UHI effects and climate change, offering actionable strategies for BEP assessment and promoting sustainable city planning.

Multi-objective optimization and inverse design of complementary field-effect transistor using combined approach of machine learning and non-dominated sorting genetic algorithms for next-generation semiconductor devices

Complementary field-effect transistors (CFETs), which are structures in which different types of transistors are vertically stacked with a shared control gate, are being focused on for continuing to satisfy Moore's law by overcoming the limitations in pitch scaling. The structures of semiconductor devices become more complex as technology node shrinks, and interrelated multivariate parameters increase. In addition, predicting problems and proposing solutions by identifying complex patterns within extensive data collected for emerging semiconductor designs pose significant computational challenges and are inherently difficult. As a breakthrough in design technology co-optimization for advanced devices, this study developed a novel optimization framework integrating technology computer-aided design simulations, machine learning, and non-dominated sorting genetic algorithms. The developed framework provides unbiased optimal solutions, even in a high-dimensional objective space, while considering the tradeoff relationships between multiple variables. In addition, it enables inverse design to identify the design parameters of devices that satisfy specific electrical performance criteria using only a forward model, while achieving an error rate of less than 2%. Using this framework, we analyzed the operational mechanism of CFETs by comparing the inverse designs of various devices. This novel approach is particularly important when the design space is complex and extensive and is well suited for developing devices that emerge with technological advancements in the semiconductor industry.

Tuning equations for sliding mode controllers: An optimal multi-objective approach for non-minimum phase systems

This article proposes an approach to create tuning equations based on optimal multi-objective design specifically for Sliding Mode controllers applied to non-minimum phase processes that can be approximated to a First-Order-Plus-Deadtime class of systems. This is achieved by taking into account various performance criteria, including tracking, controller action, and transient response. The proposed tuning equations aim to achieve a well-balanced trade-off among competing objectives. The paper presents a systematic framework for formulating the multi-objective optimization problem and derives analytical expressions for the tuning parameters based on the desired performance specifications. The effectiveness of the proposed method is demonstrated through simulations in three dynamic processes: a high-order linear system with dominant delay, a high-order plus inverse response system, and a nonlinear process with variable parameters and dominant deadtime. A comparative analysis with an existing SMC tuning technique shows superior performance results. This paper provides valuable information on the optimization procedure for obtaining analytical tuning equations for controllers, offering a promising avenue for improving the performance of control systems in practical applications.

An inerter-enhanced bistable nonlinear energy sink for seismic response control of building structures

Structural control strategies have attracted great attention in reducing unwanted structural responses. To mitigate the vibrations of seismically excited buildings, this paper proposes a novel vibration control device: an inerter-enhanced bistable nonlinear energy sink (BNESI). A three-story building structure is selected as an example, where the BNESI is attached to the top of the structure and linked to the top second floor of the structure, to explore the control effect of the device under earthquakes. The restoring force of the BNESI and equations of motion for the BNESI-controlled system are derived first. Subsequently, a multi-objective optimization design method of the BNESI is proposed and applied in building structures. The input energy robustness of the BNESI is then accessed by various initial velocities subjected to the structures. Moreover, the seismic vibration mitigation capacity of the BNESI is systematically investigated under a group of near-field and far-field excitations. Analysis results show that the proposed design algorithm for BNESI can be an attractive alternative to effectively obtain optimal parameters of the control device. The BNESI is less sensitive to the input energy levels, and can stably capture and dissipate energy from the primary structure. Also, the BNESI system provides good control effectiveness with less sensitivity to the uncertainty of seismic excitations. In addition, with less standard deviation, the damper stroke of the BNESI is only about a quarter of that of the TMD under the same excitation. Moreover, the damage to the primary structure has a negligible effect on the control effect of the BNESI system. Meanwhile, the BNESI can decrease the energy dissipation demand on the structure, and the energy distribution mode of the BNESI system shows less sensitivity to the frequency decrease of the structure.

A Multi-Objective Optimization Design for Enhancing Space Utilization and Minimizing Cutting Time in Nuclear Facility Component Decommissioning

According to the International Atomic Energy Agency’s requirements for the minimization of radioactive waste, nuclear facility components need to be cut into smaller blocks and packaged in waste containers during decommissioning. Therefore, it is important to increase the space utilization (SU) of the cut blocks in the containers while reducing the cutting time (CT). In engineering practice, increasing SU often leads to longer CTs, which poses a great challenge to improve the efficiency of the nuclear decommissioning process. To overcome this challenge and to determine the optimal operational parameters, this paper introduces a multi-objective optimization design method for cutting and packaging nuclear facility components. The goal is to increase SU while concurrently reducing CTs, ultimately minimizing the waste and reducing the decommissioning cost. First, we project the intricate three-dimensional (3D) irregular parts packing problem onto a two-dimensional plane to simplify the complexity inherent in 3D parts packing and to formulate a packing optimization model. Second, we employ the Morris method to perform global sensitivity analysis on critical parameters, including cutting angle, height, component radius, and plate thickness parameters, throughout the cutting process of nuclear facility components. This analysis produces global sensitivity indicators for each parameter, facilitating a precise assessment of the sensitivity values associated with different operational parameters. Finally, we formulate a multi-objective design optimization model for cutting and packing nuclear facility components that yields a series of alternative operating parameter solutions upon solving it. This methodology offers a resolution for the selection of optimal operational parameters in the decommissioning process of nuclear facility components, thereby attaining optimal outcomes in waste disposal and furnishing guidance for subsequent decommissioning activities.

A parallel variable-fidelity algorithm for efficient constrained multi-objective aerodynamic design optimization

Modern aerodynamic design optimization aims to discover optimal configurations using computational fluid dynamics under complex flow conditions, which is a typical expensive multi-objective optimization problem. The multi-objective evolutionary algorithm based on decomposition (MOEA/D) combined with efficient global optimization is a promising method but requires enhanced efficiency and faces limitations in its application to multi-objective aerodynamic design optimization (MOADO). To address the issues, an efficient parallel MOEA/D assisted with variable-fidelity optimization (VFO) is proposed for solving MOADO, called the MOEA/D-VFO algorithm. Variable-fidelity surrogates are built for objectives and constraints, achieving higher accuracy using fewer high-fidelity samples and a great number of low-fidelity samples. By retaining more good candidates, the sub-optimization problems defined by decomposing original objectives are capable of discovering more favorable samples using MOEA/D, which prompts optimization convergence. A constraint-handling strategy is developed by incorporating the probability of feasibility functions in the sub-optimizations. The selection of new samples for parallel evaluation is improved by filtering out poor candidates and selecting effective promising samples, which improves the feasibility and diversity of solved Pareto solutions. A Pareto front (PF) can be efficiently found in a single optimization run. The proposed approach is demonstrated by four analytical test functions and verified by two aerodynamic design optimizations of airfoils with and without constraints, respectively. The results indicate that the MOEA/D-VFO approach can greatly improve optimization efficiency and obtain the PF satisfying constraints within an affordable computational budget.

Multi-objective optimization design and experiment for a magnetic micro-vibration isolator considering stiffness trade-offs.

Vibration substantially degrades machining quality and measurement accuracy in ultra-precision processes, necessitating the implementation of vibration isolators to mitigate these effects. Magnetic vibration isolators represent a promising alternative due to their wide isolation frequency range, low energy consumption, and compatibility with vacuum environments. In designing the quasi-zero stiffness magnetic vibration isolator, the particle swarm optimization algorithm is employed to balance multiple performance indicators. To address the limitations of conventional optimization methods, which excessively prioritize stiffness and fail to encompass the entire performance spectrum of magnetic vibration isolators, this study presents an innovative optimization approach. This novel strategy directly enhances vibration isolation by integrating transmissibility and compliance considerations. Structural parameters for two types of magnetic vibration isolators were established through optimizations utilizing both conventional and innovative techniques. Simulations and experiments demonstrate that isolators developed using the proposed optimization method outperform those designed with conventional strategies, thereby validating the effectiveness of this advancement. The insights presented in this paper illuminate a novel and effective pathway for the conceptualization and refinement of magnetic vibration isolators.

Multi-objective optimal design of double-suction centrifugal pump impeller using agent-based models

Multi-objective optimal design of double-suction centrifugal pump impeller using agent-based models

Carbon emission warning method for machine tool manufacturing process based on dynamic adaptive EWMA control chart.

Machine tools constitute the backbone of the industrial sector, representing the largest global inventory of equipment. The carbon emissions resulting from the production of each machine tool merit attention. Effective management of carbon emissions in the machine tool manufacturing process is crucial. This paper introduces a novel method for early carbon emission warnings in the machine tool manufacturing process, utilizing an adaptive dynamic exponentially weighted moving average (EWMA) approach. This method addresses the challenges in identifying and monitoring abnormal carbon emissions, emerging from uncertainties and dynamic correlations. Utilizing dynamic sampling techniques and adaptive principles, this method constructs an adaptive dynamic EWMA control chart. The EWMA control chart incorporates a multi-objective optimization design model, concentrating on carbon emissions in the machine tool manufacturing process, and incorporates statistical, economic, and environmental objectives. To mitigate slow convergence rates and enhance optimization accuracy in complex control chart multi-objective optimization algorithms, this study proposes an enhanced Harris hawks optimization (HHO) algorithm as the solving algorithm. Finally, the application of this method is demonstrated through the monitoring of carbon emissions in the manufacturing process of a five-axis machine tool (EOC), as a case study. The results validate the method's rapid responsiveness to abnormal carbon emissions, providing alerts. This further confirms the efficacy and feasibility of the proposed approach. Ultimately, this approach offers a viable strategy for fostering environmentally conscious and high-quality growth in the machine tool industry.

Multi-objective optimization design for the carcass of marine flexible pipes based on BP neural network and genetic algorithm

Multi-objective optimization design for the carcass of marine flexible pipes based on BP neural network and genetic algorithm

Multi-Objective Optimization of the Ultrasonic Scalpel Rod and Tip with Improved Performance: Vibration Frequency, Amplitude, and Service Life

Ultrasonic scalpel design for minimally invasive surgical procedures is mainly focused on optimizing cutting performance. However, an important issue is the low fatigue life of traditional ultrasonic scalpels, which affects their long-term reliability and effectiveness and creates hidden dangers for surgery. In this study, a multi-objective optimal design for the cutting performance and fatigue life of ultrasonic scalpels was proposed using finite element analysis and fatigue simulation. The optimal design parameters of resonance frequency and amplitude were determined. By setting the transition fillet and keeping the gain structure away from the node position to enable the scalpel to have a high service life with excellent cutting performance. The frequency modulation method of setting the vibration node bosses at the node position and setting the vibration antinode grooves at the antinode position was compared. Then, the mechanism of the influence of various design elements, such as tip, shank, node position, and antinode position, on the resonance frequency, amplitude, and fatigue life of the ultrasonic scalpel was analyzed, and the optimal design principles of the ultrasonic scalpel were obtained. The proposed ultrasonic scalpel design was confirmed by simulations, impedance measurements, and liver tissue cutting experiments, demonstrating its feasibility and enhanced performance. This research introduces innovative design strategies to improve the fatigue life and performance of ultrasonic scalpels to address an important issue in minimally invasive surgery.