1,720,962 research outputs found
Surrogate approaches for aerodynamic section performance modelling
No full textThe use of surrogate models (response surface models, curve fits) of various types (radial basis functions, Gaussian process models, neural networks, support vector machines, etc.) is now an accepted way for speeding up design search and optimization in many fields of engineering that require the use of expensive computer simulations, including problems with multiple goals and multiple domains. Surrogates are also widely used in dealing with uncertainty quantification of expensive black-box codes where there are strict limits on the number of function evaluations that can be afforded in estimating the statistical properties of derived performance quantities. This paper compares and contrasts a wide range of surrogate types on an aerodynamic section data set that allows for design variation, manufacturing uncertainty, and damage in service, all solved with a high-quality industrial-strength Reynolds-averaged Navier–Stokes solver. This paper examines speed of training and model quality for different sizes of problem up to one where there are 26 input variables and nearly half a million CFD results in the available data set
Robust design optimization using surrogate models
No full textThe use of surrogate models (response surface models, curve fits) of various types (radial basis functions, Gaussian Process models, neural networks, support vector machines, etc.) is now an accepted way for speeding up Design Search and Optimization (DSO) in many fields of engineering that require the use of expensive computer simulations, including problems with multiple goals and multiple domains. Surrogates are also widely used in dealing with Uncertainty Quantification (UQ) of expensive black-box codes where there are strict limits on the number of function evaluations that can be afforded in estimating the statistical properties of derived performance quantities. Here we tackle the problem of Robust Design Optimization (RDO) from the direction of Gaussian Process models (Kriging). We contrast two previously studied models, co-Kriging and combined Kriging (sometimes called level 1 Kriging), and propose a new combined approach we term combined coKriging that attempts to make best use of the key ideas present in these methods
Embedded parameter information in conditional generative adversarial networks for compressor airfoil design
No full textThe use of conditional generative adversarial networks has become popular with the advancement of computer power, and in particular graphics processing units. Large hardware and software companies such as Google, IBM, and Facebook have been experimenting in this field for over a decade for facial recognition, image classification and pattern recognition, and deep fake facial and object design. One essential key to their success is access to large quantities of tagged and classified images, with millions of images typically being used. In contrast, relatively few similar advances have been seen in the engineering sector, mainly because engineering analyses that produce suitable images are often very expensive processes that absorb a considerable amount of effort to generate. In addition, feeding synthetically generated image data back into traditional engineering workflows is not entirely straightforward. In this paper we show how adversarial networks can be used if order 10
4 images are available and focused on the problem in hand. In particular we show how such images can then be augmented with histograms and glyphs to enhance the image content with pictorial representations of numerical data. This is shown to significantly assist the network training process on our data when used in contexts where numerical data categorizing individual images are available and numerical performance measures of products must be predicted. Crucially, it allows for follow-on analyses without the need to use artificially generated flow fields.
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Multi-fidelity simulation for secondary air system seal design in aero engines
No full textSecondary air system seals in aero engines sit at the intersection between all the major aspects of the physics of the system. Their behavior is affected by the air system, the thermal physics, the effect of flight loads and is highly dependent on the engine component movements, the operating conditions, and the supporting hardware. Due to the number of functional and physical interfaces in the engine, seal design is therefore a highly coupled multi physics problem and requires multiple iterations during the design process to converge to a solution that meets system requirements and optimizes engine specific fuel consumption.At different stages of the design process, simulation models with different levels of fidelity can be built. Due to the long runtimes of high fidelity coupled multi disciplinary models and to the iterative nature of the process, seal design in industry presents significant computational cost challenges, in particular in the phases of the design that require multiple simulation runs.Multi fidelity computational techniques for surrogate modelling and optimization such as Kriging and co Kriging have been demonstrated on a number of industrial applications and have the potential to significantly reduce the number of function evaluations for computationally expensive optimization problems, improve the accuracy of the predictions of surrogate models and allow the development of improved simulation strategies for a specific product design.This paper demonstrates the use of multi fidelity simulation techniques on aero engine secondary air system seal design and shows how these techniques can be used in the context of system, sub system and component design. This is achieved by combining results from a simple two dimensional Finite Element Analysis with those from a coupled secondary air system thermomechanical model.Depending on the stage of the design process and on the specific design decisions being made, the use of computational power in simulation often comes down to a trade off between reduced overall computational time and improved result accuracy. Multi fidelity simulation frameworks provide the environment to drive holistic choices on the simulation strategy, reducing the cost of the design and offering agility in the industrial response to market changes or new technologies. Moreover, this methodology establishes an infrastructure for updating the virtual product at each step of the product lifecycle, allowing experimental or service data to feed the system level simulation models to produce a digital twin
Robust structural design of a simplified jet engine model, using multiobjective optimization
Full text linkThis study demonstrates advances in multiobjective optimization, supporting a robustness study of a simplified jet engine structural model. The ultimate goal is to find the best structural configuration of shell thicknesses along the engine that will result in reduced reaction force variation under a range of external loads, will be as light as possible and where fuel consumption will be minimal. These are competitive objectives, some of which are of stochastic rather than deterministic in nature. The paper demonstrates that a deep level multiobjective search pays off many times the investment in time and money by providing significant design improvement
(Re-) Meshing using interpolative mapping and control point optimization
No full textThis work proposes a simple and fast approach for re-meshing the surfaces of smooth-featured geometries prior to CFD analysis. The aim is to improve mesh quality and thus the convergence and accuracy of the CFD analysis. The method is based on constructing an interpolant based on the geometry shape and then mapping a regular rectangular grid to the shape of the original geometry using that interpolant. Depending on the selected interpolation algorithm the process takes from less than a second to several minutes. The main interpolant discussed in this article is a Radial Basis Function with cubic spline basis, however other algorithms are also compared. The mesh can be optimized further using active (flexible) control points and optimization algorithms. A range of objective functions are discussed and demonstrated. The difference between re-interpolated and original meshes produces a metric function which is indicative of the mesh quality. It is shown that the method works for flat 2D surfaces, 3D surfaces and volumes
Multi-objective optimization of GENIE earth system models
No full textThe tuning of parameters in climate models is essential to provide reliable long-term forecasts of Earth system behaviour. We apply a multi-objective optimization algorithm to the problem of parameter estimation in climate models. This optimization process involves the iterative evaluation of response surface models (RSMs), followed by the execution of multiple Earth system simulations. These computations require an infrastructure that provides high-performance computing for building and searching the RSMs and high-throughput computing for the concurrent evaluation of a large number of models. Grid computing technology is therefore essential to make this algorithm practical for members of the GENIE project
Mutli-objective optimisation of GENIE Earth system models
No full textOverview:•GENIE Project•Multi-objective Optimisation•Surrogate Modelling•Grid Computing Infrastructure•Parameter Estimation for a new Ocean Mixing Scheme•Conclusion
Robust turbine blade optimization in the face of real geometric variations
No full textBecause of manufacturing variations, no real turbine blade exactly conforms to its nominal geometry. Even minimal deviations are known to affect aerodynamic performance, blade temperatures, and blade lifespan negatively. Rather than conventional deterministic design with its costly adherence to strict control of tolerance limits, robust design optimization aims to incorporate inevitable variations into the design process itself, so that both performance mean and scatter can be optimized simultaneously. Such a workflow is presented and applied in this paper to aerodynamically optimize an industrial turbine rotor blade against realistic manufacturing variations. A set of digitized three-dimensional laser scans from two turbofan engines forms the core of this study. On the basis of these deviations, the approach uses high-fidelity geometric models, nonintrusive uncertainty quantification, and efficient robust optimization with constraints to effectively locate Pareto-optimal designs. One selected robust blade is validated and shown to be desensitized to the observed manufacturing variability. The underlying measurement data are crucial to obtain realistic results and, as a consequence, are vital to design real robust turbine blades
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