Dr Patrick Okolo

This paper presents an approach to accurately characterize three-dimensional (3D) wire screen geometries as simplified two-dimensional (2D) screens for low Reynolds numbers. This is achieved by identifying 2D screen geometric features that provide appropriate approximations to a 3D realistic wire screen geometry. The simplified 2D screen geometries are obtained by varying geometric characteristics such as the streamwise pitch to diameter ratio within the range of 0≤C/D≤10≤C/D≤1 for side-by-side cylinders. Both in-line and staggered cylinders with spanwise pitch to diameter ratios ranging from 2.94≤P/D≤5.562.94≤P/D≤5.56 are examined here. A parametric study is performed for equivalent wire screen open area ratios varying within the range of 43.56%≤β≤67.26%43.56%≤β≤67.26%. Numerical flow field comparisons between a 3D wire screen and its approximate 2D simplification are performed, with results further validated against documented experiments. The equivalent 2D flow loss coefficients agree very closely with the full 3D results, where for some Reynolds numbers, they are found to be within 6% of the experimental results. Both 2D and 3D results are found to underpredict the experimental values. The 2D results are also found to be much more accurate than the well-known flow correlations that are commonly used. 2D turbulence intensities measured at 570 diameters downstream of the screen were found to have the same values as the experimental results for some Reynolds numbers and were within 10% at worst. This demonstrates a real advantage over a 3D model, where such a long numerical domain would be very computationally expensive. Out-of-phase vortex shedding patterns exist for both in-line and staggered screen configurations in the range of 0≤C/D≤10≤C/D≤1. The contribution of this work will enable design studies to perform preliminary fast analysis of the effect of wire screens when applied as flow or noise control technologies.

In recent decades, surrogate-based optimization (SBO) has been developed to replace costly models with cheap surrogates to improve efficiency. In this article, an adaptive parallel infill strategy is proposed to balance exploration and exploitation over the design space during the optimization process of SBO. Within this method, an inaccurate search strategy is adopted to optimize the surrogate models, thereby helping to locate the exploitation point. An elite archive is exploited to store superior sampling points for batch sampling, while a customized batch size determination strategy is introduced. The proposed SBO method with its adaptive parallel sampling strategy is tested on six unconstrained and five constrained analytical cases with the optimization results compared to state-of-the-art optimization algorithms. The optimization of a 582-bar tower truss system is also performed and utilized to verify the proposed SBO method. The proposed SBO with the adaptive parallel sampling strategy shows excellent performance and better stability.

Support vector machine (SVM) is regarded as one of the most effective techniques for supervised learning, while the Gaussian kernel SVM is widely utilized due to its excellent performance capabilities. To ensure high performance of models, hyperparameters, i.e. kernel width and penalty factor must be determined appropriately. This paper studies the influence of hyperparameters on the Gaussian kernel SVM when such hyperparameters attain an extreme value (0 or ∞). In order to improve computing efficiency, a parameter optimization method based on the local density and accuracy of Leave-One-Out (LOO) method are proposed. Kernel width of each sample is determined based on the local density needed to ensure a higher separability in feature space while the penalty parameter is determined by an improved grid search using the LOO method. A comparison with grid method is conducted to verify validity of the proposed method. The classification accuracy of five real-life datasets from UCI database are 0.9733, 0.9933, 0.7270, 0.6101 and 0.8867, which is slightly superior to the grid method. The results also demonstrate that this proposed method is computationally cheaper by 1 order of magnitude when compared to the grid method.

The dual-jet air curtain is a novel technology that, acting as a fluidic spoiler, can not only reduce aerodynamic noise but also, compared with a single-jet air curtain, can be designed to have low self noise. To achieve further progress, this paper proposes a combined use of the dual-jet air curtain with a perforated fairing. The concept was evaluated with wind tunnel experiments using tandem rods acting as an aerodynamic bluff body noise source creating suitably high amplitude narrowband and broadband noise output. In the experiment, two configurations of the combined use were tested, i.e. the fairing was located either upstream or downstream to the rods. It was found that the two technologies complement each other with the dual-jet air curtain reducing the high frequency turbulent shear layer noise emitted from the edge of the perforated fairing, whilst the perforated fairing serves to reduce the velocity of recirculating flow in the lee of the aircurtain, thus reducing this potential aerodynamic noise source.

An effective performance-matching design framework for solid rocket motor tailored toward satisfying various thrust-performance requirements is presented in this research paper through an innovative and specialized general-design approach developed to evaluate the general-design parameters. During the general-design stage, a combination of grain web and area ratio is selected as the design variables to be adjusted to obtain the general parameters. Based on the general parameters obtained, a grain-design stage incorporates the level-set method and simulates solid-propellant evolution and internal ballistic analysis, thereby obtaining the thrust performance. Grain-design effectiveness is determined by how closely the designed solid-rocket-motor performance matches and compares to a prespecified thrust curve. An efficient sequential-field-approximate-optimization algorithm is proposed and used to minimize the average rms error between the desired and designed thrusts. Validation of the proposed design framework is carried out by evaluating motor cases possessing different thrust requirements, and results obtained highlight the proposed framework as a practical and efficient strategy for solid-rocket-motor designs.