At present, optimization is an enabling technology in innovation. Multi-objective and multidisciplinary optimization tools are essential in the design process for real-world applications. In turbomachinery design, these approaches give insight into the design space and identify the tradeoffs between the competing performance measures. This paper describes the application of a novel multi-objective variant of the tabu search algorithm to the aerodynamic design optimization of turbomachinery blades. The aim is to improve the performance of a specific stage and eventually of the whole engine. The integrated system developed for this purpose is described. It combines the optimizer with an existing geometry parameterization scheme and a well-established computational fluid dynamics package. Its performance is illustrated through a case study in which the flow characteristics most important to the overall performance of turbomachinery blades are optimized.

Biobjective Design Optimization for Axial Compressors Using Tabu Search

Nomenclature A = area, m avert = vertical component of acceleration, m =s Cp = specific heat at constant pressure, J=kg=K c = volumetric efficiency cf = skin friction coefficient D = hydraulic diameter of scramjet combustor, m dF = injector port diameter/height, m g = local gravitational acceleration, m=s h = altitude, m, or enthalpy, J=kg Isp = specific impulse, s L = lift, N Linj = injection location, m Lmix = mixing length, m M = Mach number MW = molecular weight, kg=kmol m = mass, kg _ m = mass flow rate, kg=s P = atmospheric pressure, Pa Pr = Prandtl number p = pressure in scramjet combustor, Pa QR = heating value of fuel, J=kg R = universal gas constant, J=mol=K T = temperature, K t = time, s U = gas velocity in scramjet, m=s V = volume, m V1 = freestream velocity, m=s V1;eff = effective freestream velocity, m=s x = distance along scramjet combustor, m = ratio of specific heats " = fuel mass fraction c = combustion efficiency KE = kinetic energy efficiency m = mixing efficiency = density, kg=m

Comparison of Hydrogen and Hydrocarbon-Fueled Scramjet Engines for Orbital Insertion

Modern Engineering Design involves the deployment of many computational tools. Research on challenging real-world design problems is focused on developing improvements for the engineering design process through the integration and application of advanced computational search/optimization and analysis tools. Successful application of these methods generates vast quantities of data on potential optimum designs. To gain maximum value from the optimization process, designers need to visualise and interpret this information leading to better understanding of the complex and multimodal relations between parameters, objectives and decision-making of multiple and strongly conflicting criteria. Initial work by the authors has identified that the Parallel Coordinates interactive visualisation method has considerable potential in this regard. This methodology involves significant levels of user-interaction, making the engineering designer central to the process, rather than the passive recipient of a deluge of pre-formatted information. In the present work we have applied and demonstrated this methodology in two different aerodynamic turbomachinery design cases; a detailed 3D shape design for compressor blades, and a preliminary mean-line design for the whole compressor core. The first case comprises 26 design parameters for the parameterisation of the blade geometry, and we analysed the data produced from a three-objective optimization study, thus describing a design space with 29 dimensions. The latter case comprises 45 design parameters and two objective functions, hence developing a design space with 47 dimensions. In both cases the dimensionality can be managed quite easily in Parallel Coordinates space, and most importantly, we are able to identify interesting and crucial aspects of the relationships between the design parameters and optimum level of the objective functions under consideration. These findings guide the human designer to find answers to questions that could not even be addressed before. In this way, understanding the design leads to more intelligent decision-making and design space exploration.

Parallel coordinates in computational engineering design

An approach to support the computational aerodynamic design process is presented and demonstrated through the application of a novel multi-objective variant of the Tabu Search optimization algorithm for continuous problems to the aerodynamic design optimization of turbomachinery blades. The aim is to improve the performance of a specific stage and ultimately of the whole engine. The integrated system developed for this purpose is described. This combines the optimizer with an existing geometry parameterization scheme and a well-established CFD package. The system’s performance is illustrated through case studies – one two-dimensional, one three-dimensional – in which flow characteristics important to the overall performance of turbomachinery blades are optimized. By showing the designer the trade-off surfaces between the competing objectives, this approach provides considerable insight into the design space under consideration and presents the designer with a range of different Pareto-optimal designs for further consideration. Special emphasis is given to the dimensionality in objective function space of the optimization problem, which seeks designs that perform well for a range of flow performance metrics. The resulting compressor blades achieve their high performance by exploiting complicated physical mechanisms successfully identified through the design process. The system can readily be run on parallel computers, substantially reducing wall-clock run times – a significant benefit when tackling computationally demanding design problems. Overall optimal performance is offered by compromise designs on the Pareto trade-off surface revealed through a true multi-objective design optimization test case. Bearing in mind the continuing rapid advances in computing 1Applied Mathematics and Computing, School of Engineering, Cranfield University, UK. 2Engineering Design Centre, Department of Engineering, University of Cambridge, UK. 3Computational Fluid Dynamics Laboratory, Department of Engineering, University of Cambridge, UK. 4Computational Aerodynamic Design, School of Engineering, Cranfield University, UK. power and the benefits discussed, this approach brings the adoption of such techniques in real-world engineering design practice a step closer. keyword: Multi-objective optimization, design optimization modeling, metaheuristic continuous optimization, Tabu Search, aerodynamic design, turbomachinery, parallel computing.

Insight into High-quality Aerodynamic Design Spaces through Multi-objective Optimization

Automated optimization by means of numerical simulation is a very promising technique now mainly used for research studies. However, its application in an industrial context is not very common. Optimization of a supersonic inlet is presented in the context of the development of an actual airbreathing missile. The automated optimization loop is described in detail. The aerodynamic evaluation tool (the computational fluid dynamics software 2ES3D) is described, and its validation on realistic cases is presented. The use of the optimization loop on a realistic and complex problem is compared with classical methodology for inlet design. The results are very promising in terms of design cycle time and cost reduction.

Multicriteria Design Optimization of Integrated Three-Dimensional Supersonic Inlets

The flight performance of a ramjet-powered missile is improved through the use of an automated optimization loop relying on computational-fluid-dynamics tools. A generic supersonic airbreathing missile is first described,and its performance is assessed for a representative mission using Reynolds-averaged Navier-Stokes computations for aerodynamics prediction and theoretical engine performance models. The loop links together an optimization algorithm with an aerodynamic software computing the aerodynamic balance of the missile. Several optimizations are performed using different global algorithms such as simplex, evolutionary strategies, or genetic algorithms. The first ones are mono-objective: for each point of the mission (acceleration, cruise, and maneuver), the best inlet shape has to be found. Then multiobjective optimizations are performed in order to find the pareto front, that is, the best set of shapes satisfying the whole mission.

A. Gaiddon

Papers

Multicriteria Design Optimization of a Supersonic Inlet Based upon Global Missile Performance