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Investigation of aerodynamic shape optimization of cross-sectional body of high-speed train
(2025-12-01)
Prasert, Prapamonthon
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Sun, Zhen Xu
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Yao, Shuan Bao
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Bai, Ye
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Guo, Di Long
A train body’s cross-sectional shape has a significant impact on aerodynamic drag and operational safety in high-speed trains (HSTs). This study extracts five design variables from a real-world HST body: height, width, side arc radius, arc radius at the connection between the side and the roof, and arc radius at the connection between the side and the train’s bottom. The cross-validated Kriging surrogate model and the genetic algorithm are used to perform two types of aerodynamic optimization, with the cross-sectional area as a constraint. Cross-sectional shapes are optimized in both windless and windy conditions. Numerical results indicate that in a windless environment, the aerodynamic drag coefficient of the whole train is reduced by 2.4%; in a windy condition, the aerodynamic drag coefficient of the entire vehicle is reduced by 2.4%, and the aerodynamic lateral force of the leading car is reduced by 37.8%. These suggest that a flat and wide shape helps to reduce not only overall aerodynamic drag in a windless environment but also aerodynamic load in a windy environment, which can be accomplished by reducing the area of the side wall and top region, lowering the train body’s height, increasing its width, and lowering the radius of the side and top arcs.
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Co-Gasification of Plastic Waste Blended with Biomass: Process Modeling and Multi-Objective Optimization
(2024-09-01)
Aentung, Tanawat
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Patcharavorachot, Yaneeporn
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Wu, Wei
Mixed plastic/biomass co-gasification stands out as a promising and environmentally friendly technology, since it reduces wide solid wastes and produces green hydrogen. High-quality syngas can be obtained by virtue of the process design and optimization of a downdraft fixed-bed co-gasifier. The design is based on the actual reaction zones within a real gasifier to ensure accurate results. The methodology shows that (i) the co-gasifier modeling is validated using the adiabatic RGibbs model in Aspen Plus, (ii) the performance of the co-gasifier is evaluated using cold-gas efficiency (CGE) and carbon conversion efficiency (CCE) as indicators, and (iii) the multi-objective optimization (MOO) is employed to optimize these indicators simultaneously, utilizing a standard genetic algorithm (GA) combined with response surface methodology (RSM) to identify the Pareto frontier. The optimal conditions, resulting in a CGE of 91.78% and a CCE of 83.77% at a gasifier temperature of 967.89 °C, a steam-to-feed ratio of 1.40, and a plastic-to-biomass ratio of 74.23%, were identified using the technique for order of preference by similarity to ideal solution (TOPSIS). The inclusion of plastics enhances gasifier performance and syngas quality, leading to significant improvements in CGE and CCE values.
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A Multi-Objective Multi-Period Low-Carbon Location-Routing Problem: Improved NSGA-II Approach
(2024-01-01)
Chen, Binbin
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Zhang, Rui
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Long, Shengjie
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Sakdanuphab, Rachsak
In light of the escalating global concerns surrounding climate change, the significance of sustainable development in the realm of logistics cannot be overstated. This study undertakes the imperative task of devising strategies aimed at mitigating carbon emissions, reducing logistics costs, minimizing transportation time, and enhancing customer satisfaction. The research delves into the intricacies of an optimization model tailored for a specific iteration of the Location-Routing Problem (LRP), namely the Multi-Objective Multi-Period Low-Carbon Location-Routing Problem (MMLCLRP). This variant of the LRP takes into meticulous consideration several crucial parameters, such as the overall logistics cost, the arrival times of demand points, and carbon emissions. These factors are pivotal in determining both the optimal location for depots and the programming of routes within a multi-period planning horizon. The proposed model guarantees the long-term sustainability of logistics operations while flexibly adapting location routing decisions for each period in response to evolving market demands. To tackle the inherent complexity of this problem, an improved version of the Non-dominated Sorting Genetic Algorithm (NSGA-II) was employed. This approach integrates a pioneering similarity distance metric to quantify the resemblance between potential solutions. Additionally, a crowding clustering strategy was implemented to enhance the diversity within the NSGA-II. Empirical results illustrate the capability of the proposed optimization model in effectively harmonizing various objectives, encompassing economic, efficiency, and environmental aspects within the logistics domain. Additionally, the enhanced algorithm exhibits notable advantages in addressing the complexities inherent in the optimization model.
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Multi-Objective Reliability-Based Partial Topology Optimization of a Composite Aircraft Wing
(2023-02-01)
Sleesongsom, Suwin
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Kumar, Sumit
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Bureerat, Sujin
Reliability-based partial topology optimization (RBPTO) is a new approach for aircraft structural design; however, it brings computational complexity and makes aeroelasticity analysis quite challenging. Therefore, the present study proposed the multi-objective reliability-based partial topology optimization of a composite aircraft wing using a fuzzy-based metaheuristic (MRBPTOFBMH) approach. The objective is to obtain an optimal layout including partial topology and sizing of the aircraft wing structure. Here, an optimal aeroelastic structure is designed by taking into account the uncertain nature of material properties and the permitted transverse displacement. To increase computational efficiency in the design process, a non-probabilistic approach called a possibilistic safety index-based design optimization (PSIBDO) with fuzzy uncertainties is proposed to quantify the uncertainties due to aeroelastic and structural constraints. Various optimum partial topological shapes and sizing of aircraft wing structures with various PSI values in the range of [0.001, 1.0] have been obtained in a single optimization run. These outcomes, including deterministic and reliable optimal aircraft wing structures, demonstrate the high effectiveness of the proposed MRBPTOFBMH technique to alleviate the complexity of unconventional aircraft wing structure design. The findings also reveal the ease in cooperation of the suggested technique with a high-performance multi-objective evolutionary algorithm (MOEA) and its application in real-world multi-objective design optimization (MODO) problems with the least computational requirements against the traditional method’s multiple runs. Furthermore, the proposed methodology can generate potential aircraft wing structures in a range of m = [89.38–127.84] kg, and flutter speed = [285.61–632.78] m/s, that adhere to all the constraints requirements.
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Prediction of heat transfer and fluid flow in a cross-corrugated tube using numerical methods, artificial neural networks and genetic algorithms
(2022-03-01)
Eiamsa-ard, S.
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Chuwattanakul, V.
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Safikhani, H.
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Promthaisong, P.
In this paper, multi-objective optimization of geometric parameters of spirally-cross-corrugated (SCC) tubes is carried out using numerical methods, genetic algorithms (GAs), and artificial neural networks (ANNs). First, the turbulent flow is numerically characterized in various SCC tube geometries using a finite volume method with the realizable k−ε turbulence model. In this approach, the heat transfer coefficient and friction factor f in tubes are calculated. First, two parameters (corrugation pitch-to-diameter ratio (PR = p/D) and corrugation depth-to-diameter ratio (DR = e/D)) are examined in a turbulent flow regime that affects the strength of quadruple longitudinal vortex flows and thermal characteristics. At the final step, using the obtained polynomials for neural networks, multi-objective genetic algorithms (NSGA II) are employed for Pareto based multi-objective optimization of flow parameters in such tubes. This analysis considers two conflicting parameters, f Re and Nusselt number Nu with respect to three design variables, Reynolds number Re, values of PR and DR. Some interesting and important relationships between the parameters and variables mentioned above emerge as useful optimal design principles involved in the heat transfer of such tubes through Pareto based multi-objective optimization. Such important optimal principles would not have been obtained without the use of a combination of numerical techniques, ANN modeling, and the Pareto optimization.
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Multi-Objective Optimization of Mn-Doped Tio2 Content for Wettability, Printability and Intermetallic Layer of Sac305 Pb-Free Solder Paste on a Cu Substrate
(2021-12-01)
Kanlayasiri, K.
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Ariga, T.
Effects of Mn-doped TiO<inf>2</inf> nanoparticles on the wettability, printability and thickness of intermetallic layer of SAC30 on a Cu substrate were studied in this paper. Concentration of Mn-doped TiO<inf>2</inf> nanoparticles was varied from 0.05 wt.% to 1wt.%. The wettability was evaluated in terms of contact angle of solder on the copper substrate and the printability was measured from the volume ratio of printed solder on the copper substrate to volume of stencil opening. The experimental results showed that 0.10wt.% Mn-doped TiO<inf>2</inf> led to the lowest contact angle while the printability was decreased with the increase of the added nanoparticles and the thinnest intermetallic layer was found at 0.50wt.% Mn-doped TiO<inf>2</inf> nanoparticles. Mn-doped TiO<inf>2</inf> concentration was then optimized using a desirability function to find optimal values for the three responses. It was found that 0.07wt.% was the optimal concentration for the three metrics — wettability, printability and thickness of the intermetallic layer.
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