Bow shape modification through multi-objective hydrodynamic optimization: Methodology comparison between CAD-based FreeForm Deformation and Mesh-based Radial Basis Function approach

Marine industrial engineering face crucial challenges because of environmental footprint of vehicles, global recession, construction, and operation cost. Meanwhile, Shape optimization is the key feature to improve ship efficiency and ascertain better design. Accordingly, the present paper proposes an automated optimization framework for ship hullform modification to reduce total resistance at two cruise and sprint speeds. The case study is a bow shape of a wave-piercing bow trimaran hull. To this end, a multi-objective hydrodynamic problem needs to be solved. A combined optimization strategy using CFD hullform optimization is presented using the software tools STAR-CCM+ and SHERPA algorithm as optimizer. Furthermore, a comparison is made between CAD-based and Mesh-based parametrization techniques. Comparison between geometry regeneration methods is performed to present a practical and efficient parametrization tool. Design variables are control points of FreeForm Deformation (FFD) for CAD-based method and Radial Basis Function (RBF) for Mesh-based method. The optimization results show a 4.77% and 2.47% reduction in the total resistance at cruise and sprint speed, respectively.

Sampling Designs for Monitoring Ichthyoplankton in the Estuary Area: A Case Study on Coilia mystus in the Yangtze Estuary

A fishery-independent survey can provide detailed information for fishery assessment and management. However, the sampling design for the survey on ichthyoplankton in the estuary area is still poorly understood. In this study, we developed six stratified schemes with various sample sizes, attempting to find cost-efficient sampling designs for monitoring Coilia mystus ichthyoplankton in the Yangtze Estuary. The generalized additive model (GAM) with the Tweedie distribution was used to quantify the “true” distribution of C. mystus eggs and larvae, based on the data from the fishery-independent survey in 2019–2020. The performances of different sampling designs were evaluated by relative estimation error (REE), relative bias (RB), and coefficient of variation (CV). The results indicated that appropriate stratifications with intra-stratum homogeneity and inter-stratum heterogeneity could improve precision. The stratified schemes should be divided not only between the North Branch and South Branch but between river and sea. No less than two stratifications in the South Branch could also get better performance. The sample sizes of 45–55 were considered as the cost-efficient range. Compared to other monitoring programs, monitoring ichthyoplankton in the estuary area required a more complex stratification and a higher resolution sampling. The design ideology and optimization methodology in our study would provide references to sampling designs for ichthyoplankton in the estuary area.

Parametric Optimization of Integrated Circuit Assembly Process: An Evolutionary Computing-Based Approach

Strict demands for very tight tolerances and increasing complexity in the semiconductors’ assembly impose a need for an accurate parametric design that deals with multiple conflicting requirements. This paper presents application of the advanced optimization methodology, based on evolutionary algorithms (EAs), on two studies addressing parametric optimization of the wire bonding process in the semiconductors’ assembly. The methodology involves statistical pre-processing of the experimental data, followed by an accurate process modeling by artificial neural networks (ANNs). Using the neural model, the process parameters are optimized by four metaheuristics: the two most commonly used algorithms – genetic algorithm (GA) and simulated annealing (SA), and the two newly designed algorithms that have been rarely utilized in semiconductor assembly optimizations – teaching-learning based optimization (TLBO) and Jaya algorithm. The four algorithm performances in two wire bonding studies are benchmarked, considering the accuracy of the obtained solutions and the convergence rate. In addition, influence of the algorithm hyper-parameters on the algorithms effectiveness is rigorously discussed, and the directions for the algorithm selection and settings are suggested. The results from two studies clearly indicate superiority of the TLBO and Jaya algorithms over GA and SA, especially in terms of the solution accuracy and the built-in algorithm robustness. Furthermore, the proposed evolutionary computing-based optimization methodology significantly outperforms the four frequently used methods from the literature, explicitly demonstrating effectiveness and accuracy in locating global optimum for delicate optimization problems.

Formation control method based on artificial potential fields for aircraft flight simulation

As simulation becomes more present in the military context for variate purposes, the need for accurate behaviors is of paramount importance. In the air domain, a noteworthy behavior relates to how a group of aircraft moves in a coordinated way. This can be defined as formation flying, which, combined with a move-to-goal behavior, is the focus of this work. The objective of the formation control problem considered is to ensure that simulated aircraft fly autonomously, seeking a formation, while moving toward a goal waypoint. For that, we propose the use of artificial potential fields, which reduce the complexities that implementing a complete cognition model could pose. These fields define forces that control the movement of the entities into formation and to the prescribed waypoint. Our formation control approach is parameterizable, allowing modifications that translate how the aircraft prioritize its sub-behaviors. Instead of defining this prioritization on an empirical basis, we elaborate metrics to evaluate the chosen parameters. From these metrics, we use an optimization methodology to find the best parameter values for a set of scenarios. Thus, our main contribution is bringing together artificial potential fields and simulation optimization to achieve more robust results for simulated military aircraft to fly in formation. We use a large set of scenarios for the optimization process, which evaluates its objective function through the simulations. The results show that the use of the proposed approach may generate gains of up to 27% if compared to arbitrarily selected parameters, with respect to one of the metrics adopted. In addition, we were able to observe that, for the scenarios considered, the presence of a formation leader was an obstacle to achieving the best results, demonstrating that our approach may lead to conclusions with direct operational impacts.

Influence of the Final Ratio on the Consumption of an Electric Vehicle under Conditions of Standardized Driving Cycles

Electric vehicles must improve their electric drive system efficiency and effectively use their limited energy to become a viable means of transportation. As such, these technologies have undergone substantial improvements from their initial conception. More efficient powertrains, together with improved storage technologies, have enabled more extended autonomy. However, from an engineering perspective, these systems are still a key area of research and optimization. This work presents a powertrain optimization methodology, developing energy savings and improving the performance of the electric vehicle by focusing on the differential. The proposed methodology includes a study of the dynamics of the electric vehicle and the generation of a mathematical model that represents it. By simulating the vehicle and varying the final ratio of the differential, a significant optimization for energy savings is obtained by developing a standardized driving cycle. In this case, NEDC, WLTC-2, and WLTC-3 test cycles are used. The results show that a short ratio improves performance, even if this implies a larger torque from the prime mover. Depending on the operating cycle used, an energy-saving between 3% and 8% was registered. An extended energy autonomy and an increment in the life-cycle of the batteries are expected in real driving scenarios.

Numerical analysis of a radial hybrid lobe bearing with gas lubrication

В работе рассмотрены результаты численного исследования модели радиального гибридного подшипника с газовой смазкой. Установлено наличие оптимальных значений параметров, определяющих форму смазочного зазора, получены их значения и закономерности влияния на них различных характеристик подшипника. Рассмотрено распределение давления в смазочном слое при различном количестве секторов лепестков. Подтверждено предположение о наличии оптимальных значений параметров, определяющих форму смазочного зазора. И опровергнуто предположение о том, что оптимальные значения этих параметров будут зависеть от значений других параметров подшипника. Выявлен положительный эффект от совместного воздействия газодинамического и газостатического эффектов. Относительно газостатических параметров – выявлен параметр, имеющий оптимальное значение (расстояние от выбранного торца подшипника до линии сетки, на которой располагаются питатели ряда с номером р), которое сохраняет постоянное значение. На основе полученных результатов разработаны основы методики расчета и оптимизации радиальных гибридных подшипников с газовой смазкой, имеющих профилированную рабочую поверхность. The paper considers the results of numerical study of gas-lubricated hybrid radial bearing model. The presence of optimal values of parameters determining the shape of the lubrication gap is established, their values and regularities of influence of different bearing characteristics on them are obtained. Pressure distribution in the lubricating layer at different number of lobe sectors has been considered. The assumption of optimum values of parameters determining the form of the lubrication gap is confirmed. And the assumption that the optimal values of these parameters will depend on the values of other bearing parameters is refuted. The positive effect of the combined effect of gas-dynamic and gas-static effects has been revealed. Concerning gas-static parameters - the parameter which has optimum value (distance from the chosen end face of a bearing to a grid line on which feeders of a row with number p are located) which keeps constant value has been revealed. On the basis of the obtained results the bases of calculation and optimization methodology of radial hybrid bearings with gas lubrication, having profiled working surface, are developed.

Parameter design of PLA/Wood Fused Filament Fabrication using Taguchi optimization methodology

This study investigates the effects of four variables during fused filament fabrication of organic biocompatible composite material, PLA with coconut flour, at the ultimate tensile strength and elasticity module of the printed parts. The parameter optimization uses Taguchi L18 design and regression models. The examined deposition variables are the layer thickness, the nozzle temperature, the raster deposition angle, and filament printing speed. The effects of the above variables on the strength of the parts are essential to enhance the mechanical response of the printed parts. The experimental outcomes are investigated using the ANOM and ANOVA analysis and modeled utilizing linear regression models. In addition, an independent experiment was repeated three times at optimum parameters' levels to evaluate the methodology, giving predictions errors less than 3%.