The decay of a jet discharging from a circular nozzle parallel to and displaced from a solid surface is investigated under conditions where the transitional process from circular-jet flow to oblate wall-jet flow begins in the initial, transition or self-preserving regions of the original jet. The influence of displacement of the nozzle from the plane on the developed three-dimensional wall jet downstream is demonstrated and it is found that the transitional interaction with the plane is more extended when the plane interacts first in the initial zone of the circular jet. Measurements of turbulence and Reynolds stress show the transverse mixing parallel to the plane to exceed that perpendicular to the plane, and are generally consistent with the spreading rates in these two directions, the ratio of which approaches 8·5 at large distances from the nozzle. It is shown that the interaction between the plane and jet involves a relatively large-scale coherent motion in which components of velocity directed towards or away from the surface are associated with outflow or inflow along the surface. This motion is more extended in the direction parallel to the surface and provides a mechanism for the increases in mixing rate in the direction parallel to the plane.

The process of aerodynamic shape optimisation requires the development of intelligent models to address the stipulated design goals. The Direct Numeric Optimisation (DNO) approach is examined in this paper, which analyses the feasibility of a shape, in iteration until convergence based on defined objectives and constraints. The method is computationally intensive hence the components of the DNO architecture are defined, validated and modified to generate an efficient search optimisation model. Efficiency is enhanced by mapping the solution space for High-Altitude Long Endurance (HALE) airfoil design problem, through an inverse mapping of PARSEC airfoil shape variables over a series of benchmark profiles. Solution regions with aerodynamically infeasible shapes are identified and eliminated from the search process, to reduce computational time. A single-point airfoil optimisation with Gradient-Based method, over the defined search space is examined. Variations in base airfoils confirmed the solution space is highly multimodal and gradient methods merely locate the local optima. A Particle Swarm Optimisation (PSO) algorithm incorporating a double-mutation operator to mitigate sub-optimal solutions, for highly multimodal solution topologies was defined and validated. The swarm algorithm for airfoil shape optimisation confirmed the limited search flexibility of gradient methods, by establishing a global solution with a 16% reduction in drag. The swarm algorithm is computationally intense for shape optimisation. An Artificial Neural Network (ANN) is developed and validated with a relationship between the mapped PARSEC solution space and the aerodynamic coefficients of lift and drag established. A network sensitivity study indicated a double-layered network with 30 neurons for lift and 20 for drag is required to establish the aerodynamic coefficients with acceptable accuracy. The surrogate model is used for airfoil shape optimisation by replacing the flow solver from the DNO loop. Time savings are established with the aerodynamic performance of the output solution in line with the results of the direct PSO-Flow-Solver combination. Neural network simulations for fitness function approximation are prone to errors. Hence, future research will focus on developing a hybrid search methodology by integrating the flow solver and ANN in the DNO approach.

The Direct Numerical Optimization (DNO) approach for airfoil shape design requires the integration of modules: a) A geometrical shape function; b) Computational flow solver and; c) Search model for shape optimization. These modules operate iteratively until convergence based on defined objectives and constraints. The DNO architecture is to be validated to ensure efficient optimization simulations and is the focus of this paper. The PARSEC airfoil shape function is first validated by observing the effect of design coefficients on airfoil geometry and aerodynamics. The design variables provide independent one-to-one control over airfoil geometry, for imposing shape constraints. The aerodynamic performance of PARSEC airfoils through variable perturbations, conform to established aerodynamic principles. It confirms the design flexibility of the shape function in providing direct control over airfoil geometry. The Particle Swarm Optimization (PSO) algorithm is introduced as the search agent. A PSO simulation requires userinputs to define the search pattern. A methodology is presented to validate these parameters on pre-defined benchmark mathematical functions. Self Organizing Maps (SOM) are applied to illustrate trade-offs between PSO search variables. An Adaptive Inertia Weight (APSO) scheme that dynamically alters the search path of the swarm by monitoring the position of the particles, provides an acceptable convergence. Validation tests indicated the maximum velocity of the particles is less than 1% of computational domain size for convergence. The DNO approach is computationally inefficient, thus a surrogate model to address this issue is presented. An Artificial Neural Network (ANN) model with a training dataset of 3000 airfoils is applied to develop a model that applies the PARSEC airfoil geometry variables as inputs and the equating aerodynamic coefficient as output. System validation with 1000 randomly generated airfoils indicated 70% of the simulated solutions were within 10% of actual solver run. Future research will involve reducing the percentage error of the surrogate model against the theoretical solution.

The ‘sliding block’ asymmetric nozzle, which is mechanically simple and robust, is ideal for a small, variable Mach number supersonic wind tunnel installation, due the fact that it is affordable and relatively easy to manufacture. Until now there has been no rational procedure for selecting the correct nozzle profiles to satisfy certain predetermined nozzle performance criteria, ie, a range of operating Mach numbers and an acceptable test section flow non-uniformity over the given range. The present paper considers the minimum requirements which are sufficient and necessary to design an asymmetric nozzle and shows that these can be met by nozzles defined in terms of a shape parameter, an angular parameter and a length parameter. The lower end of the range of operating Mach numbers is determined by the choice of the overall flow turning angle. For a family of nozzles defined by a particular shape parameter, an heuristic analysis indicates how the test section flow non-uniformity must vary as a function of a variable which is a particular grouping of the angular and length parameters, and the range of operating Mach numbers. The dependence of the nozzle performance on the parameters is investigated numerically, assuming inviscid flow and utilising the method of characteristics. The result shows that the best nozzle shape is where the flow is expanded very rapidly immediately downstream of the throat. For a particular family of nozzles, the numerical analysis shows the complex relationship between nozzle performance and the angular and length parameters, which is best described in a graphical representation. As a result, the task of selecting correct profiles for sliding block nozzles is reduced to simply selecting the appropriate values of nozzle parameters from the curves supplied.

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Exploring the entire Pareto frontier of high-fidelity multidisciplinary problems can be prohibitive due to the excessive number of expensive evaluations required. The use of surrogate models offers promise toward managing such problems, which are restricted by a computational budget. In this paper, the kriging-assisted user-preference multi-objective particle swarm heuristic is presented, in which less accurate but inexpensive surrogate models are used cooperatively with the precise but expensive objective functions to alleviate the computational burden. A userpreference module is integrated into the optimization framework, which guides the swarm toward preferred regions of the Pareto frontier, thereby focusing all computing effort on identifying only solutions of interest to the designer. While providing a logical criterion to prescreen candidates for precise evaluation, the additional guidance provided by user-preferences guarantees an accelerated convergence rate. To depict the proficiency of the proposed framework, a suite of test problems, including the multidisciplinary cross-sectional design of a semimonocoque fuselage enclosing a pressurized cabin and payload bay, is presented. A parametric model is described that is capable of generating a broad range of double-lobe fuselage designs. The superiority of the kriging-assisted user-preference multi-objective particle swarm optimization algorithm over more traditional search methods to efficiently manage high-fidelity discontinuous design problems is highlighted.

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Intelligent shape optimisation architecture is developed, validated and applied in the design of high-altitude long endurance aerofoil (HALE). The direct numeric optimisation (DNO) approach integrating a geometrical shape parameterisation model coupled to a validated flow solver and a population based search algorithm are applied in the design process. The merit of the DNO methodology is measured by computational time efficiency and feasibility of the optimal solution. Gradient based optimisers are not suitable for multi-modal solution topologies. Thus, a novel particle swarm optimiser with adaptive mutation (AM-PSO) is developed. The effect of applying the PARSEC and a modified variant of the original function, as a shape parameterisation model on the global optimal is verified. Optimisation efficiency is addressed by mapping the solution topology for HALE aerofoil designs and by computing the sensitivity of aerofoil shape variables on the objective function. Variables with minimal influence are identified and eliminated from shape optimisation simulations. Variable elimination has a negligible effect on the aerodynamics of the global optima, with a significant reduction in design iterations to convergence. A novel data-mining technique is further applied to verify the accuracy of the AM-PSO solutions. The post-processing analysis, to swarm optimisation solutions, indicates a hybrid optimisation methodology with the integration of global and local gradient based search methods, yields a true optima. The findings are consistent for single and multi-point designs.