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The many-objective optimization performance of the Kriging-surrogate-based evolutionary algorithm (EA), which maximizes expected hypervolume improvement (EHVI) for updating the Kriging model, is investigated and compared with those using expected improvement (EI) and estimation (EST) updating criteria in this paper. Numerical experiments are conducted in 3- to 15-objective DTLZ1-7 problems. In the experiments, an exact hypervolume calculating algorithm is used for the problems with less than six objectives. On the other hand, an approximate hypervolume calculating algorithm based on Monte Carlo sampling is adopted for the problems with more objectives. The results indicate that, in the nonconstrained case, EHVI is a highly competitive updating criterion for the Kriging model and EA based many-objective optimization, especially when the test problem is complex and the number of objectives or design variables is large. [ABSTRACT FROM AUTHOR]

Abstract: This paper describes the concurrent optimization of the design of a discus and the skill with which it is thrown. The objective function for optimization is the flight distance, where longer is better. Thirteen control variables are considered, twelve of which are concerned with the skill of the thrower. These determine the launch conditions, which are controlled by the thrower when he or she throws the discus. The final variable is concerned with the discus itself. This is the moment of inertia on its transverse axis. The optimization was carried out with the aid of a genetic algorithm, and the entire trend for each generation between the objective function and the control variables could be visualized with the aid of self organizing maps. It was found that the flight distance achieved with concurrent optimization was longer than that optimized for skill alone. In the case of the optimal flight, the angle of attack should always be less than the stalling angle. [Copyright &y& Elsevier]

Abstract: To determine the flight path of a discus when it is thrown, it is essential to know what aerodynamic forces are acting on it. However, little data on this is available. Employing a full-size model in a wind tunnel, we have measured the aerodynamic forces acting on a discus spinning on its transverse axis, as well as on a ‘non-spinning’ discus. The aerodynamic force data (drag, lift and pitching moment) were obtained to create an ‘aerodynamic database’. These forces are given as functions over practical ranges of the angle of attack, the spin rate and the wind speed. It was found that the stalling angle was about 30°C, and that the lift coefficient increased linearly with increasing angle of attack up to the stalling angle. The estimated lift coefficient obtained by the vortex lattice method agrees with the experimental data within a standard deviation of 0.08. It was observed by oil flow and smoke observation methods that a pair of longitudinal vortexes occur from the ‘pressure side’ to the ‘suction side’, similar to wing-tip vortices. These longitudinal vortexes prevent flow-separation up to a high angle of attack, around 30°C. The drag coefficient increases with increasing angle of attack up to the stalling angle, and it decreases slightly around the stalling angle, corresponding to a significant and abrupt decrease in lift at the same angle. The coefficient of the pitching moment is almost positive, which is the case with ‘nose-up’ rotation. There is little difference between the aerodynamic coefficients whether the discus is spinning or not. [Copyright &y& Elsevier]

Abstract: We succeeded in obtaining PSP (Pressure-Sensitive Paint) measurements for a rugby ball. It was found that the seam of the ball is the trigger for initiating low pressure. Therefore, a slower spinning ball fluctuates during flight because of the asymmetrical pressure distribution on the sides of the ball. Based on the results from PSP measurements, multi-objective optimization of the fluctuating punted kick was carried out, and then the trade-offs between the objective functions and the control variables could be visualized using Self-Organizing Maps. It was found that the higher the spin rate at launch the greater the number of fluctuations; however, the hang time becomes shorter. [Copyright &y& Elsevier]

Carbon nanotubes (CNTs) are novel materials with extraordinary mechanical properties. To gain insight on the design of high-mechanical-performance CNT-reinforced composites, the optimal structure of CNTs with high nominal tensile strength was determined in this study, where the nominal values correspond to the cross-sectional area of the entire specimen, including the hollow core. By using machine learning-assisted high-throughput molecular dynamics (HTMD) simulation, the relationship among the following structural parameters/properties was investigated: diameter, number of walls, chirality, and crosslink density. A database, comprising the various tensile test simulation results, was analyzed using a self-organizing map (SOM). It was observed that the influence of crosslink density on the nominal tensile strength tends to gradually decrease from the outside to the inside; generally, the crosslink density between the outermost wall and its adjacent wall is highly significant. In particular, based on our calculation conditions, five-walled, armchair-type CNTs with an outer diameter of 43.39 Å and crosslink densities (between the inner wall and outer wall) of 1.38 ± 1.16%, 1.13 ± 0.69%, 1.54 ± 0.57%, and 1.36 ± 0.35% were believed to be the optimal structure, with the nominal tensile strength and nominal Young's modulus reaching approximately 58–64 GPa and 677–698 GPa. [ABSTRACT FROM AUTHOR]

This paper proposes a variant of gradient-enhanced surrogate model based on polynomial-chaos-kriging (PCK) to assist aerodynamic design exploration. The main aim is to improve the accuracy of kriging when gradient information is available via the adjoint method. In contrast to the standard gradient-enhanced kriging (GEK), the gradient information is used to enrich both the trend function and the stochastic part, using the polynomial-chaos expansion (PCE) as the trend function. The optimal polynomial terms are selected by scanning through various polynomial orders in which the coefficients are calculated by means of the generalized least squares or the least angle regression algorithm. The performance of the gradient-enhanced PCK (GEPCK) was compared with GEK, gradient-enhanced PCE (GEPCE), and their nongradient counterparts on a set of analytical and a suite of aerodynamic problems consisting of aerodynamic design exploration of a blended-wing-body configuration, an inviscid transonic flow over an airfoil, and a low-fidelity aerostructural wing design. The results show notable improvements obtained by GEPCK in terms of performance and robustness compared with other gradient-enhanced surrogate models. Based on these results, it can be concluded that enhancing the trend part with gradient is highly beneficial in improving the accuracy of the kriging surrogate model. [ABSTRACT FROM AUTHOR]