1,720,974 research outputs found
Enhancements to global design optimization techniques
No full textModern engineering design optimization relies to a large extent on computer simulations of physical phenomena. The computational cost of such high-fidelity physics-based analyses typically places a strict limit on the number of candidate designs that can be evaluated during the optimization process. The more global the scope of the search, the greater are the demands placed by this limited budget on the efficiency of the optimization algorithm. This thesis proposes a number of enhancements to two popular classes of global optimizers. First, we put forward a generic algorithm template that combines population-based stochastic global search techniques with local hillclimbers in a Lamarckian learning framework. We then test a specific implementation of this template on a simple aerodynamic design problem, where we also investigate the feasibility of using an adjoint flow-solver in this type of global optimization. In the second part of this work we look at optimizers based on low-cost global surrogate models of the objective function. We propose a heuristic that enables efficient parallelisation of such strategies (based on the expected improvement infill selection criterion). We then look at how the scope of surrogate-based optimizers can be controlled and how they can be set up for high efficiency
Exploiting patterns in the Kulfan transformations of supercritical airfoils
No full textFor a parametric airfoil to be genuinely useful in preliminary design optimization it has to satisfy a number of requirements. Perhaps most importantly the number of design variables has to be small and the design space defined by them has to exclude geometrically unrealistic shapes. Ideally, the design variables should also have intuitive significance, that is, they should be directly linked to geometrical or aerodynamic features. Furthermore, it is advantageous to have a multi-level parameterisation built into the same mathematical form, to allow design searches with increasing level of detail. Here we propose two general methods for generating airfoils that satisfy these criteria by exploiting certain patterns in the Kulfan (or class-shape function) transformations of families of existing airfoils. We illustrate the two methods by constructing concise parametric airfoils based on the NASA SC(2) family of supercritical section
Performance and noise trade-offs on a civil airliner with over-the-wing engines
Full text linkCommunity noise has become a major consideration in the design of new aircraft. The noise generated by the engines has decreased over the generations to the extent that a whole-airframe approach is required now to achieve further significant reductions. One option is to install the engines over the wings so the airframe reflects the fan noise away from on-the-ground observers. However, in addition to good noise shielding performance, the position of the engine also has to satisfy aerodynamic efficiency criteria. We investigate the sensitivity of aerodynamic and acoustic performance metrics with respect to the positioning of the engine relative to the wing. More specifically, we trade drag computed via Reynolds-Averaged Navier Stokes simulations versus noise shielding performance, obtained experimentally through scale model tests conducted in an anechoic chamber. Surrogate models of both metrics are constructed, enabling their Pareto analysis on the specific case of a modified DLR F6 airframe geometry
Supervised learning approach to parametric computer-aided design geometry repair
No full textMultidisciplinary optimization systems rely increasingly on parametric CAD engines to supply the geometries
required by their analysis components. Such parametric geometry models usually result from an uneasy compromise
between high flexibility, that is, the ability to morph into a wide variety of topologies and shapes, and robustness, the ability to produce feasible, sensible topologies and shapes throughout most of the design space. It is argued that a possible means of achieving both objectives is via a supervised learning system attached to the CAD model. It is shown that such a model can capture some of the engineering and geometrical judgment of the designer and can thereafter be used to repair design variable sets that lead to infeasible CAD models
Multidisciplinary design optimization of UAV airframes
No full textIf one considers the problem of converting an aircraft mission profile into an airframe design from an optimization theory perspective, it becomes obvious that the search problem comes with all the trimmings. The design space is large and multidimensional, there are multiple and often highly multimodal objectives and constraints, these depending not only on the design variables, but often on each other as well. Multidisciplinary Design Optimization studies can be conducted at different levels of detail, depending on the chosen trade-off between the size of the design space and the fidelity of the analysis. In this paper we discuss some of the challenges arising at the conceptual level, where simple, but versatile models and low cost analysis tools are used to guide the designer through the first, fundamental decisions of the design process. At the centre of our proposed design workflow lies a parametric geometry, residing in an off-the-shelf Computer-Aided Design (CAD) tool - this provides the models required by the multidisciplinary analyses. We also touch on some of the issues specific to the design of our chosen class of aircraft - Unmanned Air Vehicles (UAVs). To summarize: a CAD-based UAV conceptual design framework is proposed and demonstrated
A grid-based problem solving environment that uses the Master/Worker paradigm to parallelize DoE/RSM/Data-Fusion search computations
No full textThe long term aim of this work has been to prototype an Engineering Design PSE that can handle the broadening range of type of design studies being undertaken by BAE Systems, Rolls-Royce and the University Technology Partnership (UTP) for Design at Southampton University. Increasingly, we with to optimize designs with multi-fidelity, multi-disciplinary analysis methods, and where the analysis is computationally very time consuming. This means providing tools and intuitive interfaces to enable users to setup their applications in a seamless fashion, through the use of existing installed analysis codes and solvers. It also means being able to exploit Grid based and other computational clusters through the use of new distributed systems and parallel running methodologies (eg Globus software, Web Services, task farming etc
A knowledge-based geometry repair system for robust parametric CAD models
Full text linkIn modern multi-objective design optimization (MDO) an effective geometry engine is
becoming an essential tool and its performance has a significant impact on the entire MDO
process. Building a parametric geometry requires difficult compromises between the conflicting
goals of robustness and flexibility. This article presents a method of improving the
robustness of parametric geometry models by capturing and modeling engineering knowledge
with a support vector regression surrogate, and deploying it automatically for the
search of a more robust design alternative while trying to maintain the original design
intent. Design engineers are given the opportunity to choose from a range of optimized
designs that balance the ‘health’ of the repaired geometry and the original design intent.
The prototype system is tested on a 2D intake design repair example and shows the potential
to reduce the reliance on human design experts in the conceptual design phase and
improve the stability of the optimization cycle. It also helps speed up the design process
by reducing the time and computational power that could be wasted on flawed geometries
or frequent human intervention
Genetic programming approaches for solving elliptic partial differential equations
No full textIn this paper, we propose a technique based on genetic programming (GP) for meshfree solution of elliptic partial differential equations. We employ the least-squares collocation principle to define an appropriate objective function, which is optimized using GP. Two approaches are presented for the repair of the symbolic expression for the field variables evolved by the GP algorithm to ensure that the governing equations as well as the boundary conditions are satisfied. In the case of problems defined on geometrically simple domains, we augment the solution evolved by GP with additional terms, such that the boundary conditions are satisfied by construction. To satisfy the boundary conditions for geometrically irregular domains, we combine the GP model with a radial basis function network. We improve the computational efficiency and accuracy of both techniques with gradient boosting, a technique originally developed by the machine learning community. Numerical studies are presented for operator problems on regular and irregular boundaries to illustrate the performance of the proposed algorithms
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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