作者：
Peri, DanieleCNR
Ist Applicazioni Calcolo M Picone Via Taurini 19 I-00185 Rome Italy
In this paper, a multidisciplinary design optimization algorithm, the Normal Boundary Intersection (NBI) method, is applied to the design of some devices of a sailing yacht. The full Pareto front is identified for two...
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In this paper, a multidisciplinary design optimization algorithm, the Normal Boundary Intersection (NBI) method, is applied to the design of some devices of a sailing yacht. The full Pareto front is identified for two different design problems, and the optimal configurations are compared with standard devices. The great efficiency of the optimization algorithm is demonstrated by the wideness and density of the identified Pareto front.
A methodology based on the characterization of aggregate's physical properties is proposed to meet the diverse demands for compression resistance, flexural resistance, and cost-effectiveness of various recycled ag...
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A methodology based on the characterization of aggregate's physical properties is proposed to meet the diverse demands for compression resistance, flexural resistance, and cost-effectiveness of various recycled aggregate concrete (RAC). Firstly, the physical properties of three recycled aggregates (RA) with different proportions are investigated. The effects of coarse and fine aggregate types, RA content, and polyvinyl alcohol (PVA) additions on the mechanical properties of RAC are studied. Secondly, a database is created based on the experimental results. Potential combinations of input parameters are selected using correlation coefficient and collinearity diagnosis. Moreover, a neural network handles the complex nonlinear relationship between the input parameters and the target requirements. Thirdly, the Pareto frontier is solved by combining the algorithm with the highest fitting accuracy as the objective function with the Non-dominated Sorting Genetic Algorithm II (NSGA-II). Finally, the Ideal Point Method is employed to search for Pareto solutions to find the optimal mix ratio schemes with different preferences. The proposed method achieves a maximum error of 10.27 % between actual and calculated concrete mix costs, validating its effectiveness. It can simultaneously optimize various RAC mix proportions to achieve desired mechanical and economic targets.
The development, design, examination, and optimization of carbon-free power generation models are essential to achieve a sustainable future with net-zero emissions. This study introduces a novel multigeneration system...
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The development, design, examination, and optimization of carbon-free power generation models are essential to achieve a sustainable future with net-zero emissions. This study introduces a novel multigeneration system, uniquely combining a supercritical CO2 Brayton cycle and a transcritical CO2 Rankine cycle, supported by a solar parabolic trough collector. The system integrates a reverse osmosis desalination unit, enabling simultaneous production of clean water, heating, and power. A multi-objective optimization framework is implemented by the NSGA-II algorithm, coupled with the TOPSIS method, to explore and identify optimal operational conditions. The innovation lies in the comprehensive consideration of solar incident angles and their impact on system performance, a rarely addressed aspect in the literature. Detailed thermodynamic analysis highlights system performance, achieving a net power capacity of 1052 kW, freshwater generation of 90.44 m3/h, and hot water generation of 1614 kW. The optimized results demonstrate significant improvements in overall energy (50.28 %) and exergy efficiency (22.31 %), showcasing the system's potential as a benchmark for sustainable, zero-emission energy solutions.
Because of the advantages of high power factor and high power, and has been gradually applied in the field of industrial drive. The permanent magnet assisted synchronous reluctance motor has been widely applied in the...
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Because of the advantages of high power factor and high power, and has been gradually applied in the field of industrial drive. The permanent magnet assisted synchronous reluctance motor has been widely applied in the field of industrial drive. The drive motor requires large output torque, high power factor and small torque ripple, thereby imposing more stringent demands on motor optimization. However, due to the complex rotor structure with the complex magnetic barrier, the optimization parameters of the permanent magnet assisted synchronous reluctance motor is large. Aiming at the above-mentioned problems, a three-step optimization method is studied. The relationship between the rotor structural dimensions is studied to reduce the number of parameters to be optimized. The parameter sensitivity is used to optimize the structure parameters. The response surface method and genetic algorithm are combined used to realize the comprehensive optimization of multi-objective. Then, the parameters with high sensitivity of single target are optimized by the single parameter scanning method. Finally, the structural detail of the magnetic barrier tip is precisely optimized to reduce the torque ripple. A 15 kW/1500 rpm permanent magnet assisted synchronous reluctance motor is optimized by the three-step optimization method. The simulation and experimental results are presented to verify the improvement of the motor performance.
Metal is commonly used due to its high absolute energy absorption (EA) value and low mass specific energy absorption (SEA(m)), while carbon fiber reinforced polymer (CFRP) boasts a high mass SEA(m) value but is costly...
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Metal is commonly used due to its high absolute energy absorption (EA) value and low mass specific energy absorption (SEA(m)), while carbon fiber reinforced polymer (CFRP) boasts a high mass SEA(m) value but is costly. To address this, this paper suggests the application of fiber metal laminate (FML) material in automotive battery packs, as it is lightweight, with a high mass SEA(m) and a moderate cost. Regarding the ground impact of a car that poses a threat to battery safety, the impact resistance of FML planes is investigated. A comparison is made among the collision responses of the battery pack enclosures made of three materials, with the same thickness and mass, showing that FML is suitable as a battery pack shell material. Furthermore, based on the kriging model and non-dominated sorting genetic algorithm II (NSGA-II), a multi-objective optimization design is developed to minimize the impact displacement and mass of the battery pack enclosures with FML by optimizing the thickness of metal and fiber layers. The Pareto frontier is obtained, leading to a design modification that decreases the mass and the impact displacement compared to the initial design, as well as the improvement of the collision performance of the specific energy absorption.
The optimal design of automobile seats plays an important role in passenger safety in high-speed accidents. In order to enhance the accuracy of the prediction of the input variables and output response of the seat, a ...
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The optimal design of automobile seats plays an important role in passenger safety in high-speed accidents. In order to enhance the accuracy of the prediction of the input variables and output response of the seat, a hybrid machine learning prediction model that combines the improved gray wolf optimizer (IGWO) and back propagation neural network (BPNN) has been proposed, and the prediction effect of the model was validated using the seat simulation data. Initially, based on the experimental data, finite element models were developed for eight typical working conditions of automobile seats and their accuracy was validated. Subsequently, the energy absorption to mass ratio method was employed to screen the design variables, resulting in the selection of 17 thickness variables and 15 material variables. Thereafter, the gray wolf optimizer (GWO) algorithm underwent enhancement through the incorporation of the dynamic leadership hierarchy (DLH) mechanism and the revision of the positional formula, yielding the IGWO algorithm. Following this, the IGWO algorithm was applied to optimize the hyperparameters of BPNN, culminating in the establishment of the IGWO-BPNN model. Ultimately, the seat multi-objective optimization design process was addressed using multi-objective gray wolf optimizer (MOGWO) to achieve the Pareto frontier, while the decision-making was conducted using the combined compromise solution (CoCoSo) method to determine the best trade-off solution. Furthermore, the effectiveness of the proposed optimal design method is evidenced by comparing the baseline design, simulation analysis, and optimal design methods. The results indicate that the optimized automotive seat frame achieves a reduction in cost by 20.7 % and mass by 22.9 %, simultaneously maintaining safety performance. Consequently, the proposed optimization design methodology is demonstrated to be highly effective for the multi-objective optimization design of automotive seat frames.
Nuclear Thermal Propulsion (NTP) offers significant advances over conventional chemical propulsion systems, providing high thrust, high specific impulse, long endurance and reusability. These capabilities are well sui...
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Nuclear Thermal Propulsion (NTP) offers significant advances over conventional chemical propulsion systems, providing high thrust, high specific impulse, long endurance and reusability. These capabilities are well suited to the increasing requirements of future space missions. This study focused on the medium-thrust, solid-state, closed-cycle NTP engine, selecting nine critical design parameters for a comprehensive multi-objective optimization (MOO) analysis based on three steady-state performance metrics by implementing the Non-dominated Sorting Genetic Algorithm III (NSGA-III) algorithm and coupling the SCTRAN code to calculate the steadystate thermal-hydraulic parameters. The results presented a range of optimal engine configurations. For scenarios with two predefined mission objectives, the study recommends optimal values for a third performance objective in addition to the nine most important system design parameters. The optimal designs showed a reduced global sensitivity for certain parameters, thereby increasing the robustness of the system. The reliability of the optimization approach was confirmed by comparing one of the study's detailed steady-state results with existing literature. The paper concluded with key optimization recommendations that are instructive for future NTP engine design and refinement.
The weight loss process variables of alkali-treated micropolyester woven fabrics were optimized and reported in this study. The gray relational analysis with the help of the Taguchi technique was efficiently used to o...
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The weight loss process variables of alkali-treated micropolyester woven fabrics were optimized and reported in this study. The gray relational analysis with the help of the Taguchi technique was efficiently used to optimize the key variables of this process. The caustic soda concentration, treatment temperature, and weight loss machine speed were considered the control or design parameters. The weight reduction percentage, air permeability, tensile strength, and thermal resistance of alkali-treated woven polyester fabrics were also considered as responses in this study. The experiments were implemented according to a 33 full factorial design. The levels of the control parameters which yield the maximum weight reduction, tensile strength, air permeability, and minimum thermal resistance of the treated polyester fabrics were found to be the sodium hydroxide concentration, and treatment temperature with the highest levels, and machine speed with the lowest level. This means that a 27% caustic soda concentration, treatment temperature of 125 degrees C, and machine speed of 40 m/min exhibited the optimum properties of the treated micropolyester fabrics. It was also proved that the treatment temperature is the most influential factor affecting the micropolyester fabric's properties. The confirmation test, which was carried out in this study, confirmed that the gray relational analysis improved the alkali-treated polyester fabric properties.
Electromagnetic wiping systems allow to pre-meter the coating thickness of the liquid metal on a moving substrate. These systems have the potential to provide more uniform coating and significantly higher production r...
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Electromagnetic wiping systems allow to pre-meter the coating thickness of the liquid metal on a moving substrate. These systems have the potential to provide more uniform coating and significantly higher production rates compared to pneumatic wiping, but they require substantially larger amounts of energy. This work presents a multi-objective optimization accounting for (1) maximal wiping efficiency (2) maximal smoothness of the wiping meniscus, and (3) minimal Joule heating. We present the Pareto front, identifying the best wiping conditions given a set of weights for the three competing objectives. The optimization was based on a 1D steady-state integral model, whose prediction scales according to the Hartmann number (Ha). The optimization uses a multi-gradient approach, with gradients computed with a combination of finite differences and variational methods. The results show that the wiping efficiency depends solely on Ha and not on the magnetic field distribution. Moreover, we show that the liquid thickness becomes insensitive to the intensity of the magnetic field above a certain threshold and that the current distribution (hence the Joule heating) is mildly affected by the magnetic field's intensity and shape.
Rotary tillage is a critical process in agricultural production. However, discrepancies between the chassis movement and the operational parameters of tillage equipment often result in unstable tillage quality, reduce...
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Rotary tillage is a critical process in agricultural production. However, discrepancies between the chassis movement and the operational parameters of tillage equipment often result in unstable tillage quality, reduced efficiency, and decreased endurance of electric tractors. To address these challenges, this study proposes a multiobjectiveoptimization method that integrates the fuzzy analytic hierarchy process (FAHP) with an improved grey wolf optimizer (IGWO). A tillage quality monitoring system was developed by integrating and processing multi-sensor data to measure the soil fragmentation rate, tillage depth stability coefficient, and power consumption per unit area for various forward speeds, tillage depths, and roller speeds. Regression models were then established based on the collected data. FAHP was employed to determine the influence weights of these models on tillage quality and to construct a multi-objective optimization function. A traversal optimization algorithm based on adaptive dynamic weights and a reverse-learning grey wolf optimization strategy was proposed to identify the optimal working curves for forward and roller speeds under different target tillage depths. The results demonstrate that the proposed algorithm effectively optimizes multi-objective operational parameters, reducing the number of iterations by five and improving accuracy by 20.33% compared to previous methods. Field tests confirmed that the optimal soil fragmentation rate, tillage depth stability coefficient, and power consumption per unit area at different target tillage depths met operational standards for soil fragmentation and tillage depth stability. These findings provide a theoretical foundation and practical guidance for reducing operational tillage resistance and energy consumption in electric horticultural machinery.
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